XXX10.1177/1529100612436522Finkel et al.Online Dating2012

Research Article

Online Dating: A Critical Analysis From the
Perspective of Psychological Science

Psychological Science in the
Public Interest
13(1) 3–66
© The Author(s) 2012
Reprints and permission:
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DOI: 10.1177/1529100612436522
http://pspi.sagepub.com

Eli J. Finkel1, Paul W. Eastwick2, Benjamin R. Karney3, Harry T. Reis4,and Susan Sprecher5
1

Northwestern University; 2Texas A&M University; 3University of California, Los Angeles;
University of Rochester; and 5Illinois State University

4

Summary
Online dating sites frequently claim that they havefundamentally altered the dating landscape for the better. This
article employs psychological science to examine (a) whether
online dating is fundamentally different from conventional
offline dating and (b) whether online dating promotes betterromantic outcomes than conventional offline dating. The
answer to the first question (uniqueness) is yes, and the answer
to the second question (superiority) is yes and no.
To understand how online dating fundamentally differsfrom conventional offline dating and the circumstances underwhich online dating promotes better romantic outcomes thanconventional offline dating, we consider the three major
services online dating sites offer: access, communication,and matching. Access refers to users’ exposure to andopportunity to evaluate potential romantic partners they areotherwise unlikely to encounter. Communication refers tousers’ opportunity to use various forms of computer-mediatedcommunication (CMC) to interact with specific potential
partners through the dating site before meeting face-to-face.
Matching refers to a site’s use of a mathematical algorithm toselect potential partners for users.
Regarding the uniqueness question, the ways in which
online dating sites implement these three services have indeed
fundamentally altered the dating landscape. In particular,
online dating, which has rapidly become a pervasive meansof seeking potential partners, has altered both the romantic
acquaintance process and the compatibility matching process.
For example, rather than meeting potential partners, gettinga snapshot impression of how well one interacts with them,and then slowly learning various facts about them, online
dating typically involves learning a broad range of facts about
potential partners before deciding whether one wants to meetthem in person. Rather than relying on the intuition of villageelders, family members, or friends or to select which pairs of
unacquainted singles will be especially compatible, certain
forms of online dating involve placing one’s romantic fate in
the hands of a mathematical matching algorithm.
Turning to the superiority question, online dating has
important advantages over conventional offline dating. For
example, it offers unprecedented (and remarkably convenient)levels of access to potential partners, which is especiallyhelpful for singles who might otherwise lack such access. It
also allows online daters to use CMC to garner an initial senseof their compatibility with potential partners before deciding
whether to meet them face-to-face. In addition, certain datingsites may be able to collect data that allow them to banish fromthe dating pool people who are likely to be poor relationshippartners in general.
On the other hand, the ways online dating sites typically
implement the services of access, communication, andmatching do not always improve romantic outcomes; indeed,
they sometimes undermine such outcomes. Regarding access,encountering potential partners via online dating profiles
reduces three-dimensional people to two-dimensional displaysof information, and these displays fail to capture thoseexperiential aspects of social interaction that are essential
to evaluating one’s compatibility with potential partners. In
addition, the ready access to a large pool of potential partners
can elicit an evaluative, assessment-oriented mindset that
leads online daters to objectify potential partners and mighteven undermine their willingness to commit to one of them. It
can also cause people to make lazy, ill-advised decisions whenselecting among the large array of potential partners.
Regarding communication, although online daters canbenefit from having short-term CMC with potential partners
before meeting them face-to-face, longer periods of CMC
prior to a face-to-face meeting may actually hurt people’s
romantic prospects. In particular, people tend to overinterpretthe social cues available in CMC, and if CMC proceeds
unabated without a face-to-face reality check, subsequentface-to-face meetings can produce unpleasant expectancy
violations. As CMC lacks the experiential richness of a faceto-face encounter, some important information about potential
partners is impossible to glean from CMC alone; most users
will want to meet a potential partner in person to integrate
their CMC and face-to-face impressions into a coherent whole
before pursuing a romantic relationship.
Regarding matching, no compelling evidence supports
matching sites’ claims that mathematical algorithms work—that they foster romantic outcomes that are superior tothose fostered by other means of pairing partners. Part of

4
the problem is that matching sites build their mathematicalalgorithms around principles—typically similarity but also
complementarity—that are much less important to relationshipwell-being than has long been assumed. In addition, these sites
are in a poor position to know how the two partners will growand mature over time, what life circumstances they will confrontand coping responses they will exhibit in the future, and how
the dynamics of their interaction will ultimately promote orundermine romantic attraction and long-term relationshipwell-being. As such, it is unlikely that any matching algorithm
that seeks to match two people based on information available
before they are aware of each other can account for more thana very small proportion of the variance in long-term romantic
outcomes, such as relationship satisfaction and stability.
In short, online dating has radically altered the datinglandscape since its inception 15 to 20 years ago. Some of thechanges have improved romantic outcomes, but many havenot. We conclude by (a) discussing the implications of online
dating for how people think about romantic relationships andfor homogamy (similarity of partners) in marriage and (b)offering recommendations for policymakers and for singles
seeking to make the most out of their online dating endeavors.

Introduction
For as long as humans have recognized the urge to form romantic
relationships, they have also recognized that finding an appropriate partner can be challenging, and that sometimes it is useful toget some help. From the Jewish shadchan immortalized in themusical Fiddler on the Roof, to the khastegari customs of Iran, tothe arranged marriages still prevalent in parts of Southeast Asia,
there is a tradition—millennia old—of romantic relationships
arising not only from chance encounters between two individuals
but also from the deliberate intervention of third parties (Coontz,
2005). For most of those millennia, the resources available tothese third parties remained the same: a broad social network,strong opinions about the sorts of people who belong together,and the willingness to apply those judgments to the formation of
actual couples (Ahuvia & Adelman, 1992).
In the modern age, the desire to find a romantic partner
endures, as does the sense that doing so can be challenging. But
the resources available for meeting these challenges havechanged, and many of these changes can be traced to the invention, spread, and now ubiquity of the Internet. According to recent
data, some 30% of the 7 billion people on our planet now haveaccess to the Internet (InternetWorldStats.com, 2011). In North
America, where Internet usage is highest, that figure reaches
78%. Every domain of contemporary life, from commerce andpolitics to culture, is now touched by the Internet in some way.
With respect to forming romantic relationships, the potential to reach out to nearly 2 billion other people offers several
opportunities to the relationship-seeker that are unprecedented

Finkel et al.
in human history. First, whereas the “field of eligibles” (Kerckhoff, 1964) for an individual was once limited primarily tomembers of that individual’s social network, the Internet now
affords access to a vastly wider network of potential partners
who would have been unknown or inaccessible in former eras.
Second, whereas interaction between potential partners once
depended on their proximity to each other, the Internet now
facilitates nearly instantaneous communication via multiple
channels (i.e., text, voice, image, and video) without partners
having to be in the same location and even without partners’
conscious awareness (e.g., by allowing others to view one’s
information online). Third, whereas the choice of a mate once
relied largely upon the individual’s intuitions and personal
opinions, the Internet promises to create matches between
suitable partners using new tools that draw upon data provided
by thousands, or millions, of users.
Recognizing the unique possibilities afforded by the Internet, numerous commercial Web sites have arisen to provide
these services to users seeking romantic relationships. Specifically, the past 15 to 20 years have witnessed the developmentof Web-based companies that specialize in providing somecombination of:a. access to potential romantic partners
b. communication with potential romantic partners
c. matching with compatible romantic partners.
Each year, millions of hopeful relationship seekers use thesesites, often paying substantial fees for the privilege.
To attract customers, online dating sites typically emphasize two aspects of the services they offer. First, they emphasize that their services are unique to dating through theInternet; that is, the sites are offering a service that cannot be
duplicated in any other way. The homepage of PlentyOfFish,for example, claims that membership on the site gets youaccess to “145 million monthly visitors” and that “you are notgoing to find any other site that has more singles looking tomeet new people” (PlentyOfFish.com, 2011). Presumably thatclaim refers not only to other Web sites but also to other venues where single people gather to meet, such as bars, parties,
churches, or libraries. Second, online dating sites emphasizethat forming relationships using their services is superior todating offline. The Web site for eHarmony, for example,
asserts that the services the site offers “deliver more than justdates”; instead, it promises connections to “singles who havebeen prescreened on . . . scientific predictors of relationshipsuccess” (eHarmony.com, 2011b, para. 1). The implication isthat eHarmony possesses knowledge about relationships thatmost people lack and that applying this knowledge will lead tomore favorable relationships than subscribers would experience without this knowledge. The OkCupid Website also
implies access to knowledge unavailable to the layperson with

Corresponding Author:
Eli J. Finkel, Northwestern University, 2029 Sheridan Road, Swift Hall #102, Evanston, IL 60208-2710
E-mail: finkel@northwestern.edu

Online Dating
the straightforward claim, “We use math to get you dates”(OkCupid.com, 2011). By referring to millions of users, science, and math, online dating sites suggest that meetingromantic partners online is not only different from, but also
better than, searching for partners in conventional ways.
Each of these claims raises questions that can be answered
empirically. For example, with respect to uniqueness, does therise of online dating represent a fundamental change in theprocess of forming and maintaining romantic relationships?
With respect to superiority, are the users of online dating sites
in fact improving their chances of experiencing positive
romantic outcomes compared to individuals who rely entirely
on more conventional methods of meeting partners?
Addressing such questions is of great public importance forseveral reasons. First, romantic relationships—their presence,as well as success or failure—play a central role in individuals’ physical and emotional well-being. The need to connect
deeply with others has been described as a “fundamental
human motivation” (Baumeister & Leary, 1995). When thatneed is fulfilled by a satisfying intimate relationship, couples
experience better health (Cohen et al., 1998), recover from illnesses more quickly (Kiecolt-Glaser et al., 2005), and live longer (Gallo, Troxel, Matthews, & Kuller, 2003; Holt-Lunstad,
Smith, & Layton, 2010). Indeed, the presence of a satisfying
intimate relationship is one of the strongest predictors of happiness and emotional well-being that has been measured (Diener & Seligman, 2002). Loneliness and distressed relationships,
in contrast, predict increased risks of depression and illness(Cacioppo et al., 2002) and incur enormous national costs interms of lost productivity (Forthofer, Markman, Cox, Stanley, &Kessler, 1996), and they are the leading reasons why people
seek therapy or help from lay counselors in the United States(Veroff, Kulka, & Douvan, 1981). Thus, online dating sites aretreading in deep waters, and whatever the implications of thesesites, those implications are likely to have strong ripple effects.
Second, as commercial dating sites become increasingly
accepted as a means of forming romantic relationships, more
and more couples are meeting online (Rosenfeld, 2010). One
industry trade report estimated that almost 25 million uniqueusers around the world accessed an online dating site in April,2011 alone (Subscription Site Insider, 2011). If some of theindividuals who form relationships online would not otherwise have found partners, then the availability of the uniqueservices that the Internet provides may be a boon to relationship seekers. Moreover, if relationships formed through theInternet are in fact superior to those formed via more conventional means, then the increasing popularity of online datingsites has the potential to boost happiness and to reduce thegreat suffering and costs associated with relationship distressand dissolution (e.g., Amato & DeBoer, 2001; Forthofer et al.,
1996; Kiecolt-Glaser et al., 2005; Sbarra, Law, & Portley,2011). If the claims of online dating sites are unfounded, however, then increasing numbers of people are pursuing relationships that are actually no better than matches formed offline
and that may even be worse.

5
A third reason to evaluate the claims of online dating sites
is that online dating now consumes vast resources in the
United States and around the world. Online dating has growninto a billion-dollar industry, and it is one of the few growthindustries during a period of worldwide recession (Visualeconomics.com, 2011). In pursuit of these revenues, online datingsites spend hundreds of millions of dollars annually to promote the value of the services they provide (Nielsenwire.com,
2009). Believing these messages, millions of users are not
only spending their money on memberships and subscriptions,but they are also investing considerable time. One estimate
suggests that users spend an average of 22 minutes each timethey visit an online dating site (Mitchell, 2009), and anothersuggests that they spend 12 hours per week engaged incomputer-based online dating activity (Frost, Chance, Norton,& Ariely, 2008). Across millions of users, this represents anenormous allocation of time that might otherwise be spent onother activities, including engaging in social interactions
offline. These costs in time and money are warranted if online
dating actually provides improved, cost-effective access to
successful romantic relationships. If such evidence is lacking,however, then people seeking romantic partners may be wasting significant time and money that they could direct toward
more productive activities.
There is now a strong foundation of scientific researchfrom which to evaluate the implications of online dating forthe initiation and development of romantic relationships. This
research spans multiple domains, many of which directly
investigate personal relationships. Although the scholarly literature on personal relationships is relatively young (for an
historical analysis, see Reis, 2012), it already spans the disciplines of clinical, developmental, and social-personality psychology; sociology; communication; and family studies; andreaches into various other disciplines as well. In addition toresearch that directly addresses relationships, decades ofresearch on topics such as decision making, interpersonalcommunication, and motivated cognition also provide relevant findings. Extrapolating from these literatures, and drawing upon the nascent literature on online dating specifically,
we can now examine how the advent of the Internet is affecting processes and outcomes relevant to romantic relationships.
Moreover, we can compare the results of this body of research
to the specific claims of online dating sites, critically evaluating the degree to which these claims are supported by scientific evidence.
The overarching goal of this article is to draw upon theaccumulated scientific literature on romantic relationships andother psychological phenomena to evaluate (a) whether online
dating represents a fundamental rather than an incremental
shift in the process of relationship initiation (the uniquenessquestion) and (b) whether online dating yields better romantic
outcomes than does conventional offline dating (the superiority question). In pursuit of this broad goal, we begin by providing an overview of the present analysis, elaborating upon thethree key services of online dating (access, communication,

6and matching), addressing issues of scope, and defining key
terms. Next, we address the two major questions we seek toanswer. Part I compares and contrasts online dating with conventional offline dating in terms of pervasiveness, the acquaintance process, and compatibility matching, concluding thatonline dating is fundamentally different from conventional
offline dating on all three of these fronts. Part II examines
whether online dating yields romantic outcomes that are superior to those emerging from conventional offline dating. Thissection demonstrates that the claims of superiority made byonline dating sites lack scientific validity, and it scours diverse
scientific literatures to discern the ways in which the access,communication, and matching offered by online dating sites
improve versus undermine romantic outcomes. After addressing these two major questions, we discuss implications of
online dating for how people think about and approach romantic relationships, for homogamy (similarity of partners) inmarriage, and for public policy. Finally, we offer recommendations for relationship seekers.

Overview
Online dating’s three key services
As discussed previously, dating sites provide some combination of three broad classes of services: access, communication,and matching (for a similar tripartite typology, see Ahuvia &Adelman, 1992). Access refers to users’ exposure to andopportunity to evaluate potential romantic partners whom they
are otherwise unlikely to encounter. Specifically, dating sites
typically accumulate profiles—Web pages that provide information about potential partners—that users can browse.
Because many sites have thousands, sometimes millions, ofusers, online dating offers access to a larger number of potential partners than anybody could have access to in the offline
world. In principle, users can contact any of these new potential partners through the dating site, although, in practice,many of the potential partners to whom users are given accessmight not reply. As such, the access that users acquire throughdating sites does not necessarily yield access to a relationshippartner; rather, it simply alerts users to the existence of available partners.
Communication refers to users’ opportunity to use various
forms of computer-mediated communication (CMC) to interact with specific potential partners on the dating site before
meeting face-to-face. The mechanisms of communication varyconsiderably across the online dating landscape. Asynchronous forms of communication, including messaging systems
that approximate e-mail and simpler, less personalized formsof communication (e.g., virtual “winks”) that quickly and concisely convey some measure of interest, are commonplace.
Alternatively, users may also choose real-time, synchronous
forms of communication, such as live instant-message (textbased) chat and live interaction via webcams that allows users
to see and hear each other.

Finkel et al.
Matching refers to a site’s use of a mathematical algorithm
to identify potential partners, called “matches,” for theirusers. These matches are presented to the user not as a random selection of potential partners in the local area but rather
as potential partners with whom the user will be especiallylikely to experience positive romantic outcomes. A key
assumption underlying matching algorithms is that somepairs of potential partners will ultimately experience betterromantic outcomes, in the short term or the long term (orboth), than other pairs of potential partners because the individuals are more romantically compatible from the start.
Another assumption is that the seeds of this compatibility canbe assessed using self-reports or other types of individualdifference measures before two people even become aware ofeach other’s existence. If these assumptions are valid, then an
algorithm directing users’ attention to the smaller pool ofpotential partners with whom they are especially compatible
would be useful, increasing the likelihood of, efficiency withwhich, or degree to which users achieve relationship success.
Although all sites offer some degree of access and communication, many sites do not offer matching.
In this article, we draw upon research in psychology andrelated disciplines to answer the uniqueness and superiority
questions. This task would be straightforward if scholars had
conducted controlled experiments investigating how the presence or implementation of access, communication, or matching services offered at dating sites alters the dating process oryields superior romantic outcomes compared to conventional
offline dating. Consider, for example, a hypothetical online
dating “clinical trial.” Researchers might randomly assign
single participants to pursue romantic partners by either (a)using a matching service, perhaps one already in use at a particular dating site or one created by the research team; or (b)exploring their romantic options using whatever offline
options they choose—akin to a wait-list control. Unfortunately, to our knowledge, no such study exists.
Nevertheless, even without controlled experimental studies
that compare online with offline dating, a vast scientific literature can address the degree to which the two dating contexts
differ and whether those differences are likely to alter romantic outcomes. To extend the clinical-trial metaphor, scholars
have amassed considerable knowledge about the many “active
ingredients” of each specific implementation of access, communication, and matching, even in the absence of clinical trials of specific forms of online dating per se. Although it would
be best to have scientific studies of both (a) the functioning of
the whole product in an experimental setting (as is typicallythe case with pharmaceuticals, for example) and (b) the underlying active ingredients, this article by necessity focuses only
on the workings of the online dating active ingredients—specific implementations of access, communication, and matching. Many of the workings of online dating sites are shrouded
in proprietary mystery, but reviewing the extant scientific literature to investigate the active ingredients can yield important insights.

Online Dating

Scope
Our task was not to provide a comprehensive topography ofthe online dating landscape. This landscape is constantly
changing—new sites are created and old sites go out of business, change forms and names, and have facelifts—so any
attempt to be comprehensive would achieve immediate obsolescence.1 By focusing broadly on the ways dating sites implement the services of access, communication, and matching, wewere able to examine the psychological essence of online dating without becoming preoccupied with any particular claimof any particular site (although we did not shy away fromexamining particular claims where doing so was instructive).
Many online dating sites offer services beyond access, communication, and matching, including dating advice, personality assessment, and, on occasion, summaries of scientificstudies of romantic relationships. Although these features
could have important benefits, we excluded them from thisanalysis both because they are readily accessible outside ofonline dating sites (e.g., through self-help books) and because
their influence involves individual daters obtaining new
knowledge rather than processes occurring between two
potential daters.
In addition, our goal was not to review all Internet sites
through which people could conceivably meet someone
online for a romantic relationship. As presented in Table 1,
there is a huge variety of Internet sites that individuals could
use to meet potential romantic partners. We focused on thosesites with the explicit and primary goal of introducing singles
to potential romantic partners who are hoping to form dating
and perhaps marital relationships. They included self-selection sites in which people browse profiles of potential partners, either from the general population of possible online
daters (Row 1 in Table 1) or from a particular subpopulation(Row 2); sites that allow users’ family members or friends toplay matchmaker for them (Row 3); sites that allow for live
interaction, either through webcam-based video dating (Row4) or avatar-based virtual dating (Row 5); matching sites
based primarily either on users’ self-report data (Row 6) or on
non-self-report data, such as genetic data (Row 7); and globalpositioning-system-based smartphone apps (Row 8). We did
not examine general personal advertising sites where the formation of romantic relationships is a by-product of the site’s
main function (Row 9), sex or hookup sites (Row 10), infidelity sites (Row 11), sites for arranging group dates (Row 12),
general social networking sites (Row 13), or massively multiplayer online games (Row 14). In addition, our primaryemphasis was on online dating as it is practiced in the UnitedStates and other Western countries, which means certain typesof online dating sites that are prevalent elsewhere (e.g., matrimonial sites in India) were beyond the scope of this article.
Finally, also beyond our scope was speed-dating, a dating
approach developed in the 1990s in which singles attend an
event where they engage in a series of brief face-to-face interactions with a series of potential romantic partners and decide

7
whether they would (“yes”) or would not (“no”) be willing to
get together with each of them in the future (Eastwick & Finkel, 2008b; Finkel, Eastwick, & Matthews, 2007).
Furthermore, we did not seek to provide an exhaustive
review of all studies that have been conducted on the topic of
online dating. This article is less a review of the online dating
literature than an empirically based analysis of whether online
dating represents a fundamental change in the process ofromantic relationship initiation and whether the forms ofaccess, communication, and matching offered by online dating are likely to improve romantic outcomes. In cases wherescholars have not conducted the optimal empirical investigations in the romantic domain in general or in the online dating
domain in particular, we extrapolated from related scholarly
literatures to address our major questions of uniqueness andsuperiority.

Definitions
Before addressing these two questions, we define several
important terms beyond those we have already defined (access,communication, matching, and profiles). We use the term dating sites to refer to those Web sites that primarily focus onoffering the user opportunities to form a new romantic relationship that has the potential to become a dating and perhaps
a long-term committed relationship, such as marriage (i.e., thetop half of Table 1). We use the term online dating, sometimes
called Internet dating, to refer to the practice of using datingsites to find a romantic partner.2 Throughout the manuscript,
we frequently compare online dating with conventional offline
dating. This term encompasses the myriad ways that people
meet potential romantic partners in their everyday lives
through non-Internet activities—through their social network(e.g., a mutual friend introducing two single people to eachother), a chance face-to-face encounter (e.g., approaching a
new coworker or a stranger at a coffee shop), or some combination of the two (e.g., chatting with a friend-of-a-friend at aparty).3 Although conventional offline dating is a heterogeneous category that comprises many contexts for meeting
potential partners (e.g., meeting at a bar vs. in church), thesecontexts collectively differ from online dating in that they do
not offer the same forms and degree of access, communication, and matching. To the extent that some precursors of dating sites share these features (e.g., video-dating, newspaper
personal ads), they are excluded from the term conventional
offline dating.
A crucial term when evaluating whether online datingyields superior outcomes to conventional offline dating is
positive romantic outcomes, which refers to the extent towhich someone positively evaluates, and/or intends to persist in pursuing, a specific (potential or current) romantic
partner and/or a specific (hypothetical or actual) relationship. This definition is deliberately broad, as the term applies
to the level of attraction someone might experience whenbrowsing a profile to the level of love someone feels toward

8

Finkel et al.

Table 1. Types of U.S. Online Dating Sites and Their Distinctive Features
Row
1
2
3
4
5
6
7
8

Type of site

Distinctive feature

Example sites

Site types within the purview of the present article
Users browse profiles of a wide range ofMatch, PlentyOfFish, OkCupid
partners
Niche self-selection sites
Users browse profiles of partners from a
JDate, Gay, SugarDaddie
specific population
Family/friend participation sites
Users’ family/friends can use the site to play
Kizmeet, HeartBroker
matchmaker for themVideo-dating sites
Users interact with partners via webcam
SpeedDate,Video dating, WooMe
Virtual dating sites
Users create an avatar and go on virtual
OmniDate, Weopia,VirtualDateSpacedates in an online setting
Matching sites using self-reports
Sites use algorithms to create matches
eHarmony, Chemistry, PerfectMatchbased on users’ self-report data
Matching sites not using self-report
Sites use algorithms to create matches
GenePartner, ScientificMatch,based on non-self-report data
FindYourFaceMate
Smartphone apps
GPS-enabled apps inform users of partners
Zoosk, Badoo, Grindr
in the vicinity
General self-selection sites

Site types beyond the scope of the present article
9
10

General personal advertisementsites
Sex or hookup sites

11

Infidelity sites

12

Sites for arranging group dates

13
14

Social networking sites
Massively multiplayer online games

Users can advertise for diverse goods andservices, including partners
Users meet partners for casual sexual
encounters
Users or partners (or both) pursue
extrarelationship affairs
Users propose get-togethers with a groupof strangers
Users can meet friends of friends
Users can meet partners using avatars in acomplex online environment

Craigslist, most newspaper sites
OnlineBootyCall, AdultFriendFinder,
GetItOn
AshleyMadison, IllicitEncounters,
WaitingRoom
Ignighter, Meetcha, GrubWithUs
Facebook, MySpace, Friendster
SecondLife, TheSims, WorldOfWarcraft

Note: The content in this table is illustrative, not comprehensive. The distinctive feature of a particular type of site does not imply that it is the sole purposeor method the site uses; many sites have multiple features or use multiple methods to help users access potential partners. In addition, due to the rapid paceof technological and entrepreneurial innovation, the methods that people use to meet potential romantic partners online are constantly changing. This table
represents a snapshot from 2011. GPS = global positioning system.

his or her long-term spouse, and everything in between. As
such, this definition encompasses both attraction contexts,
in which individuals are evaluating potential romantic partners with whom they do not yet have a romantic relationship(i.e., they are not “officially” romantic partners), and relationship contexts, in which individuals are evaluating someone with whom they already share a romantic relationship.
We refer to both contexts as romantic contexts and to therelationships people pursue in both contexts as romantic
relationships.4
Turning to the people involved in the online dating process,the term users refers to those who are pursing potential partners through online dating. The term potential romantic partner refers to any member of one’s preferred sex whom one
believes is available and interested in finding a romantic partner of the user’s sex, with the term match restricted to a potential romantic partner whom a mathematical algorithm has

selected as an especially compatible potential romantic partner
for a given user.

Part I: Is Online Dating Fundamentally
Different From Conventional Offline
Dating?
With these definitions in hand, we now turn to the first of thetwo major questions in this article: Is online dating fundamentally different from conventional offline dating (the uniqueness question)? Our goal was not to compare online dating toconventional offline dating on every possible dimension.
Rather, we focused on three crucial dimensions, examining
whether online dating represents fundamental rather thanincremental alterations of the dating landscape. First, is online
dating a pervasive means through which singles seek to meetOnline Dating
potential partners, or is it a fringe approach pursued by a small
fraction of singles? Second, does online dating fundamentally
alter the process of becoming acquainted with potential romantic partners, or is this acquaintance process largely similar in
online dating and conventional offline dating? And third, does
online dating fundamentally alter the process of compatibility
matching, or does it simply represent a variant of the same
matching procedures that professional and familial matchmakers have used for centuries?

How pervasive is online dating?
We begin to address the uniqueness question by examining
whether online dating is a pervasive versus a fringe means for
singles to meet potential romantic partners. Rather than simply
presenting a brief snapshot of present usage rates, we situatethese rates in a broader context by providing an historical
overview of online dating and discussing how societal attitudes toward online dating have evolved in recent years. This
analysis will suggest that although online dating functions as
the most recent outgrowth of an endeavor with a long history,
the rapid increases in its prevalence and mainstream acceptability over the past 15 years have resulted in a fundamental
shift in how large swaths of single people seek to meet romantic partners.
The history and prehistory of online dating. Social andcommercial institutions that facilitate courtship and marriage
are diverse and long-standing (e.g., Ahuvia & Adelman, 1992).
Matchmaking and introductory intermediaries, particularly forthe purpose of facilitating marriage, have been a component of
the marriage-courtship market long before the emergence of
online dating. In addition, computers have been used forromantic matching, both commercially and in university settings, for over 60 years.
Matching, pre-Internet. Human matchmakers, working forpay or barter, have recommended matches for centuries andare even described in the Bible. Traditional matchmaking wasoften a side role for rabbis, priests, clergy, and sometimes
elderly women in the community, and these matchmakers
were sought out by parents who were searching for spouses fortheir children. Today, human matchmakers continue to offer
matching assistance, although in the United States and in mostother Western societies, the service typically is initiated not byparents but by the single adults themselves who may be dissatisfied with their other options (including online dating) for
seeking a partner. Today’s commercial matchmakers often
work with a small base of clientele, whom they get to know
personally. Their matching decisions are typically based on
intuition and experience, not mathematical algorithms (Adelman & Ahuvia, 1991; Gottlieb, 2006; Woll & Cozby, 1987).
In addition, personal advertisements for dating and matrimony have existed for centuries. Indeed, shortly after theadvent of the modern newspaper, people used it to advertisefor a spouse (Cocks, 2009), with the first printed personal
advertisements dating to the early 1700s (Orr, 2004). Printed

9personal advertisements were especially prevalent when
unusual circumstances caused groups of unmarried individuals (predominately men) to be isolated from potential partners.
For example, American soldiers in war, beginning with the
Civil War, advertised for partners and pen pals, as did those
who settled America’s Western frontier in the early 1900s(Orr, 2004).
By the 1970s, personal advertisements were becoming
more popular, both in mainstream publications and in niche
newspapers and magazines, even in the absence of highly
asymmetric sex ratios in the immediate environment. Many
factors contributed to this trend, including a rise in age at firstmarriage, which resulted in singles seeking partners after leaving the mate-rich environments of high school and college; agrowing dependency on media for information in general andfor mating information in particular; and a consumerist shift insociety toward a service economy in which it was increasingly
acceptable for businesses to perform services once performed
primarily by individuals and families (e.g., Ahuvia & Adelman, 1992; Merskin & Huberlie, 1996; Smaill, 2004).
In printed personal advertisements, the advertiser typicallyprovided a description of his or her qualities, stated a preference for the type of relationship sought, and described a few
qualities desired in an ideal partner. The publisher charged theadvertiser based on the number of words or lines, and the
responders typically paid for the service of transferring their
responses to the advertiser. Although printed personal advertisements became more prevalent in the 1960s and 1970s, onlya small percentage of people met their partners in this way.
Nationally representative surveys conducted circa 1980
(Simenauer & Carroll, 1982) and in 1992 (Laumann, Gagnon,
Michael, & Michaels, 1994) demonstrated that only a fractionof 1% of Americans met romantic partners through personal
advertisements.
Video-dating, a channel for mate seeking that involved
members providing profile descriptions and photographs andthen participating in a brief videotaped interview, emerged in
the 1980s (Ahuvia & Adelman, 1992; Woll & Cozby, 1987).
To make their choices among dating prospects, members conducted an initial screening based on the prospects’ photos andprofile information and then viewed the videotapes of those
who most interested them. Members then expressed interestin meeting specific others, and, if the interest was mutual,contact information was exchanged and pairs could thenmeet face-to-face. As with personal advertisements, however,video-dating remained more of a fringe than a mainstream
approach to meeting partners.
Just as printed personal advertisements followed the emergence of newspapers, and just as video-dating followed theemergence of video cassette recorders, computer-based matching services followed the emergence of computers. Indeed,
computers were used for romantic matching decades before
the development of the Internet, both in conjunction with academic research on the attraction process and as a componentof commercial adventures in matching. As one example of theformer, a group of students developed the “nation’s first foray

10
into computers in love” for a final class project in a Stanford
University mathematics course in 1959 (Gillmor, 2007, p. 74).
The project, entitled “Happy Families Planning Services,”
involved programming an IBM 650 computer to pair up 49
men and 49 women for a computer-date matching party. The
men and women completed a questionnaire assessing characteristics like age, height, weight, religion, personality traits,
and hobbies, and the students wrote a program to calculate thedifference score for each possible male–female pair. The pairwith the lowest difference score was selected as the first and“best” match, but as fewer couples remained in the pool, thecouples had larger difference scores and made for some “oddcouples” (Gillmor, 2007). Although the project yielded one
eventual marriage and an “A” grade in the course, it did not
progress beyond the class assignment.
Commercial enterprises soon followed. For example, agroup of Harvard students who were unhappy with traditional
ways of meeting dates founded a corporation and, in 1965,
launched Operation Match, which was designed to use computers to identify compatible matches (Leonhardt, 2006;
Mathews, 1965). Questionnaires were sent to campuses aroundthe country, with student participation increasing when Look
Magazine ran a feature article on the company (Shalit, 1966)
and television and radio talk shows discussed the new “hightech” dating system. The questionnaire asked the students torate their own characteristics (e.g., physical attractiveness,
intelligence) and to indicate how their ideal mate would rateon those same characteristics. The student entrepreneurs
charged their student clients $3 (U.S. currency) for the promise of a list of compatible matches. Unfortunately, their rented
Avco 1790 computer, which was the size of a small room,
could not easily process the questionnaire data, resulting inlong delays. Operation Match closed its doors in 1968, but not
before its proprietors introduced several couples who eventually married (Leonhardt, 2006; Mathews, 1965).
In the 1970s, additional dating companies sought to makecomputer matching work and to build a clientele interested inpurchasing the service. For example, a 1970 advertisement for
a computer dating company made the following claim: “Utilizing the most advanced and sophisticated computer techniques with IBM 360/16, Compatibility can GUARANTEE
you 2 to 10 compatible referrals every 30 days for five fullyears” (reported in Orr, 2004, p. 22). However, these forays
into the business of computer dating also failed. The computers were not powerful enough to handle data from many users,
and there was no Internet platform for efficiently communicating with customers and obtaining data from them.
An historical analysis of the early use of computers formatching would not be complete without a description of“Project Cupid,” which involved the attempted recruitment ofa group of eminent psychologists to develop a dating service.
In July of 1969, George Levinger, a psychologist at the
University of Massachusetts and one of the pioneers of relationship science, received a visit from a psychology department chair at another university, who asked Levinger to join

Finkel et al.
with other social psychologists and develop a nonprofit computer dating entity that could also become a source for valuable research data. The department chair was representing a
wealthy trustee of his university, who was inspired by his adult
daughter’s failure to find suitable dating partners (Levinger,
personal communication, September 15, 2009). Levinger
teamed up with two other luminaries in the relationships field,
Elaine Hatfield (then Walster) and Zick Rubin, and they met in1970 in New York on a trip sponsored by the trustee, whooffered to donate $100,000 to start a nonprofit corporation.
After their second meeting, when they had reviewed numerous
matching instruments, Levinger wrote as follows (personalcommunication, September 15, 2009):
Our meeting was a mixed success. On one hand, Elaine,
Zick, and I had each reviewed numerous matching
instruments and had submitted them for the group’s
consideration. On the other hand, our reviews gave us
little confidence in our ability to devise satisfactory“matches” for our clients. . . . We were not ready topromise that our “expertise” would give them better
matches than they could find on their own.
Following two additional meetings, which included outside
experts, Levinger and the others terminated the project becauseof their concern that it would neither serve their clients well
nor generate sufficiently worthwhile research data.
By the 1980s, online communication was possible throughbulletin board systems, which collectively served as a precursor to the current Internet (Whitty, 2007). People could communicate and fall in love in cyberspace chat rooms without
meeting in person (Ben-Ze’ev, 2004; Whitty & Carr, 2006;
Whitty & Gavin, 2001). However, in the era before widespread availability of Web browsers, usage of Internet-based
bulletin boards remained a niche activity for a small group ofparticularly tech-savvy people. In short, although commercial
mating intermediaries have been around a long time and haveundergone technological advances, none of them achieved
widespread use. In particular, none of them has attained anything approximating the scale or prevalence achieved byonline dating in recent years.
Development of online dating sites. As computers became
cheaper, smaller, and more powerful, and as the Internet
became widespread, a new generation of computer dating
businesses emerged. We categorize these online dating businesses into three generations: (a) online personal advertisement sites, (b) algorithm-based matching sites, and (c)smartphone-based dating applications. The first generation
began in earnest when Match launched in 1995. Many sites
followed Match’s lead in the ensuing years, providing singles
with a broad range of options for posting and browsing
online personal advertisements. Such dating sites essentiallyfunctioned as search engines, allowing users to post a profileand to browse the profiles of potential partners. Whereas
Match and many other sites (e.g., PlentyOfFish) have a broad

Online Datinguser base, other sites, typically referred to as niche sites, aredesigned for specific subpopulations. For example, online
dating sites cater to users seeking partners of a particular age(e.g., SeniorPeopleMeet), religious orientation (e.g., JDate),sexual orientation (e.g., GaySinglesOnline), race (e.g., BlackSingles), social status (e.g., DateHarvardsq), disability status(Dating4Disabled), and hobby preference (e.g., VampireLovers). Although many online dating sites charge fees formembership, some are free, generating revenue throughadvertisements and optional services (e.g., OkCupid,
PlentyOfFish).
The second generation began when eHarmony launchedin 2000, ushering in a new online dating service: “sciencebased” online matching systems, also referred to as algorithm-based matching or compatibility matching. Manycompanies offering matching services now exist, including
PerfectMatch, which launched in 2002, and Chemistry,
which launched in 2005. These sites distinguish themselvesfrom the sites focusing on online personal advertisements byclaiming to take a scientific approach to matching. At suchsites, users are required to provide data about themselves,
frequently in the form of self-reports tapping aspects of theirpersonality, background, interests, values, and characteristics desired in a partner. The sites then process the data toprovide matches based on the site’s compatibility algorithm,
which is typically proprietary. These sites, some of whichhave hired behavioral or social scientists to assist in thematching process, typically charge higher monthly fees thanfirst-generation sites do.
Whereas the matching algorithms at sites like eHarmony,
PerfectMatch, and Chemistry appear to rely primarily uponusers’ self-reports, the matching algorithms at other sites
appear to rely heavily upon non-self-report data. For example,
some sites claim to create matches based upon genetic andimmunological compatibility between potential partners(Frazzetto, 2010). GenePartner, which launched in 2008, markets its genetic testing as a supplementary matching tool for
online dating sites, matchmakers, or even couples themselves
who, for $99, can order a kit and mail in a saliva sample to findout their score on biological compatibility (McGrane, 2009).
ScientificMatch, launched in 2007, claims to be a full-fledged
Internet matching system that combines genetic and immunological compatibility matching with personality and values
matching and users’ personal preferences, charging $2,000 for
a lifetime membership.
In recent years, the distinction between self-selection sites
and algorithm-selection sites has blurred. Recognizing thefinancial success of sites like eHarmony, many sites that began
as a platform for online personal advertisements now offer
suggested matches alongside the continued opportunity forusers to search, on their own, through hundreds or thousandsof profiles. As one example, Match has developed a matchingalgorithm that is based not only on users’ stated preferences
but also on their ratings of the matches sent to them (Gelles,
2011).

11
The third generation began in 2008, shortly after Apple Inc.
opened its App Store to coincide with the release of the second
version of the iPhone, a popular and influential smartphone.
The App Store provided a forum in which independent companies could build software programs, called “apps,” for theiPhone. Other smartphone makers soon followed suit, andnow many smartphones run a vast range of apps, including
many location-based online dating (“mobile dating”) apps that
capitalize on mobile Internet technology and global positioning system functionality to inform users of potential partners
in the immediate vicinity (e.g., Zoosk, Badoo). For example, auser at a museum or on a train can browse rudimentary profilesof potential partners, typically consisting of a photograph andsome very basic information—as long as those potential partners are members of the dating site, have made themselves
available to browsing, and are in the immediate vicinity.
These mobile dating apps, which are frequently launchedwithin broader social networking sites like Facebook, are rapidly becoming extremely popular. For example, as of late2011, Badoo’s Web site claimed it had over 133,000,000 users
worldwide, seeking to entice potential users to join “the hundreds of thousands who sign up daily” (Badoo.com, 2011).
Although users join Badoo for free, they can pay to have theirprofile given greater prominence on the site for a period oftime. According to Wired magazine, many millions of people
pay for this service, helping to increase the company’s market
valuation into the billions of U.S. dollars (Rowan, 2011).
Attitudes toward online dating. Just as the technologies tofacilitate romantic relationship initiation have changed, attitudes toward online dating have changed as well. Personal ads
in magazines and newspapers never became a widely sociallyacceptable way to search for a partner. Indeed, people who
used personal advertisements to find partners frequently did so
furtively to minimize their embarrassment (Darden & Koski,1988). The stigma associated with personal advertisements
extended initially to online dating. Although no academic
research was published on attitudes toward online dating at its
inception, online dating historians have referred to the stigma
that existed in the 1990s about seeking partners online, as wellas the perceived risks associated with doing so, including thepossibility of encountering a sexual predator or “psycho”(Anderson, 2005; Gwinnell, 1998). In addition, online dating
was assumed to be for “nerds,” “the desperate,” and the“socially inept” (Goodwin, 1990; Orr, 2004; Smaill, 2004;
Whitty & Carr, 2006; Wildermuth & Vogl-Bauer, 2007). One
commentator has suggested that there might have been a kernel of truth in these stereotypes, observing that that the early
adopters of the technology were “a little on the shy side or alittle on the sleazy side” (Orr, 2004, p. 29).
In the late 1990s and early 2000s, however, online dating
started becoming more mainstream and shedding its stigma(e.g., Harmon, 2003; Lawrence, 2004; Tracy, 2006). Movies
and other aspects of pop culture were beginning to present
online romance in a positive way. For example, the 1998 movie,

12You’ve Got Mail presented two attractive professionals (playedby Meg Ryan and Tom Hanks) who had an acrimonious business rivalry but who fell in love with each other while communicating anonymously over the Internet, unaware of eachother’s offline identity. This film “worked wonders in erasing negative stereotypes” about using the Internet to meet aromantic partner (Orr, 2004, p. 32).
Still, academic research on attitudes about online dating at
the turn of the millennium found that college students, forexample, had more negative than positive attitudes about online
dating. In one study, students expressed concern that people on
the Internet would lie, that meeting romantic partners online
was unsafe, and that it would take longer to get to know someone online than in person (Donn & Sherman, 2002). The graduate students in this study had more favorable attitudes than did
the undergraduate students, perhaps because graduate students
face a crunch in which the desire to find a life partner increases(as people age) just as the access to potential partners declines(as the college years recede further into the past). In an experimental follow-up study, these researchers randomly assigned
students either to learn minimal (written) information about
two popular dating sites or to spend several minutes (in a computer lab) taking the online introductory tour to the two sites
plus viewing a printout of the survey questions that these sites
used to develop profiles (Donn & Sherman, 2002). Participantsin the latter group, who received first-hand exposure to the dating sites, expressed a neutral impression of dating sites on average, whereas the former, who did not receive first-hand
exposure, expressed a negative impression on average. In general, attitudes toward online dating tend to grow more positive
as one’s immediate social milieu provides less bountiful accessto potential romantic partners (Doan & Meyers, 2011; Hogan,
Dutton, & Li, 2011; Madden & Lenhart, 2006; Sautter, Tippett,& Morgan, 2010).
The first large-scale study to examine public attitudes
toward online dating in a diverse sample was conducted in2005 by the Pew Internet & American Life Project, whichmonitors the effects of the Internet on Americans’ lives. As
reported by Madden and Lenhart (2006), the Pew study foundthat Internet users were divided in their views about whether
online dating is a good way to meet people (44% agreed, 44%
disagreed), although more agreed than disagreed (47% vs.
38%) that online dating allows people to find a good match
because they get to know a lot more people. In addition, only
29% of the Pew respondents believed that online daters aredesperate, although 66% believed that online dating can be
dangerous because of personal information being available on
the Internet.
In sum, although the public may have had negative attitudes toward online dating in its early years, “people no longer
think of online dating as a last resort for desperadoes andcreeps” (Paumgarten, 2011, p. 41). Attitudes and behaviors arelinked, of course, and greater experience with online dating
can lead to more positive attitudes. In addition, positive attitudes can emerge simply from exposure to online dating

Finkel et al.
through the experiences of members of one’s social network.
Positive attitudes and awareness about others’ favorable experiences can then lead to the desire to try online dating. Althoughsome stigma about online dating may still exist (e.g., Doan &Meyers, 2011), rates of participation in online dating havegrown dramatically in recent years.
Prevalence of online dating. Match asserts, in a popup window on its homepage, that “1 in 5 relationships start online”(Match.com, 2011, para. 1). Is this claim true, and on whatevidence is it based? Furthermore, if it is true, how did theUnited States transition from less than 1% of the population
meeting partners through printed personal advertisements orother commercial intermediaries in 1992 (Laumann et al.,1994) to a substantial proportion of singles meeting partners
through online dating sites today?
Some experts have suggested that the online dating industry had a slow start because of the early stigma, including thecarryover negative views based on printed personal advertisements. Because early adopters of online dating were viewed
negatively, users were embarrassed to admit that they were
trying it, and therefore “the industry for a long time enjoyedalmost no benefit of word-of-mouth referrals and moved along
at a painfully slow growth rate, taking that much longer toreach a critical mass” (Orr, 2004, p. 13). Some have argued
that 1997 was the tipping point, when the number of users
reached critical mass; usage of online dating sites thereafter
increased rapidly as people shared their dating experiences(e.g., Hogan et al., 2011). Not coincidentally, 1997 also marked
the widespread emergence of Web 2.0 technologies, which
involved more dynamic, user-centered information sharing.
A few small-scale studies conducted in the 1990s with targeted samples of Internet users (e.g., participants of online
newsgroups or online games) demonstrated that relationships
could begin online and then progress offline (e.g., Parks &Floyd, 1996; Parks & Roberts, 1998; Utz, 2000). In 2005, thePew Internet study surveyed a nationally representative sample of 3,215 Americans, Internet users and nonusers, to examine how common it was for relationships to begin online(Madden & Lenhart, 2006). Among the two thirds of participants in the sample who were married or in a committed relationship, most reported having met their partners in “realworld” settings, such as at work or school (38%); through family or friends (34%); or at a nightclub, bar, café, or other social
gathering (13%). Only 3% reported meeting their partners
through the Internet—although many of the respondents presumably met their partners before online dating was a viableoption. In contrast, among the single adults in the sample who
were Internet users and currently seeking romantic partners,
37% reported having dated online.
With an industry that is changing rapidly and with the continuing diffusion of online dating experiences through social
networks (e.g., Sautter et al., 2010), prevalence data collected
in 2005 are sorely outdated. A nationally representative surveyof 4,002 adults (including 3,009 with a spouse or romanticOnline Dating

Pervasiveness—conclusion. For many decades, people
have used technological innovations such as the invention of
the computer and video-recording devices to meet potential
romantic partners, and entrepreneurs have sought to build
profitable businesses based upon helping people do so. Thus, in
one sense, online dating represents the next step in a long-term
process of using new technologies to meet romantic partners.
However, such a view fails to appreciate that the prevalence of
online dating usage represents a fundamental departure fromall previous uses of technology to introduce people to potential
romantic partners. Whereas personal advertisements andvideo-dating never became a pervasive or socially acceptablemeans of meeting potential partners, online dating has entered
the mainstream, and it is fast shedding any lingering social
stigma. As a result, by 2005, 37% of single Internet users weredating online (a percentage that is almost certainly much higher
today), and, by 2007–2009, more new romantic relationships
had begun online than through any means other than meetingthrough friends.

25
20
15
10
5
2007–20092004–2006

1999–2003

1994–1998

1989–1993

1979–19880Pre-1979

partner) conducted in 2009 examined how people had met
their current partners (Rosenfeld, 2010; Rosenfeld & Thomas,
2010). Among the participants who met their partners between
2007 and 2009, 22% of the heterosexual couples had met on
the Internet, which made the Internet the second-mostcommon way to meet a partner, only behind meeting throughfriends. Figure 1, which shows the percent of participants inthis sample who met a partner online as a function of the year
in which the relationship began, demonstrates a steep increaseover time. Of course, not all couples who meet online actually
meet through an online dating site (others meet through social
networking sites or in other online venues), but it is likely thatonline dating accounted for much of the increase in online
meeting over the period covered in Figure 1.
We discussed earlier that several societal changes in earlier
decades contributed to single adults placing printed personal
advertisements and participating in video-dating and othercommercial avenues for finding a mate. These trends, including the delay in marriage and the increasing consumeristic
society, have continued apace. Other contributing social forces
include greater mobility in society, which creates a need tomeet partners in new ways (Sautter et al., 2010), and increasing sensitivity to sexual harassment in the workplace, which
has led to widespread implementation of rules against sexual
relationships with coworkers, potentially reducing the likelihood of finding a partner at work. Consistent with this possibility, the prevalence of meeting partners through work has
decreased since 1990 (Rosenfeld, 2010). Additional factorsthat may have contributed to an increase in online dating
include (a) technological changes that make the Internet accessible, efficient, and fun; (b) the pervasiveness of media messages, including testimonials from successful couples, aboutfinding the perfect partner through dating sites; and (c) social
network diffusion effects (Christakis & Fowler, 2009), facilitated by social network sites, in which behavioral tendencies
spread from person to person.

13

Fig. 1. Percentage of Americans who met their partners online as a functionof year met. Data are from Wave 1 of the national data set, How Couples Meetand Stay Together (collected in 2009), for 2,535 heterosexual respondents
who were married or in a romantic relationship (cohabiting, dating). The
percentages for same-sex couples were generally higher, particularly in themost recent period (61%). Adapted from Table 7 in Rosenfeld and Thomas(2010) with permission from the authors.

In short, online dating has rapidly become a pervasivemeans through which singles seek to meet potential romantic
partners. Although this fact is sufficient to allow for the conclusion that online dating has fundamentally altered the datinglandscape, it does not allow for the conclusion that the acquaintance process or the matching process has fundamentallychanged. We now examine whether online dating has fundamentally changed each of these processes as well.

Has online dating fundamentally altered theromantic acquaintance process?
The online dating process varies across dating sites and acrossusers. Figure 2 depicts this process in a prototypical, idealized
form. This idealized version of the process, which involves
nine steps, begins with a potential user seeking information
about one or more dating sites and ends with him or her developing a relationship with a partner met through a dating site. In
discussing the mechanics of the process, we rely upon several
sources, published and unpublished, academic and journalistic(e.g., Baker, 2008; Fiore & Donath, 2004; Gottlieb, 2006;
Lawson & Leck, 2006). In particular, we rely upon Bridget
Long’s (2010) dissertation, which garnered insights into thepsychological experience of online dating through both (a) an
ethnographic content analysis of eHarmony, Match, and PlentyOfFish and (b) in-depth interviews of online daters. Our
analysis will suggest that online dating has indeed fundamentally altered the romantic acquaintance process.
Online dating: A nine-step process. As we progress throughthe steps depicted in Figure 2, we compare and contrast theonline dating process with more conventional (offline) formsof dating. Toward this end, we consider three prototypicalways in which a person might meet a stranger in his or her

14

Finkel et al.

4. Browse
Others’Profiles
1. Seek Info
about One orMore ODS(s)2. Register
for One orMore ODS(s)5. Initiate
Contact
through ODS

3. Create Profile on One orMore ODS(s)7. Engage in
Mutual Mediated Comm.

8. MeetFace-ToFace

9. Develop
an Offline
Relationship

6. Receive
Contact
through ODS

Fig. 2. The nine steps in the prototypical, idealized online dating process.The solid arrows represent the sequence in logical order. A given user can be
at various steps in this process with multiple potential partners simultaneously (e.g., at Step 7 with one potential partner and at Step 5 with another).
The two solid arrows emerging from Step 3 illustrate the two pathways through which a user can receive contact from potential partners through thedating site: (a) indirectly by browsing others’ profiles (Step 4), initiating contact with one or more of them (Step 5), and receiving one or more replies
through the dating site (Step 6); or (b) directly by receiving contact from one or more potential partners through the dating site (Step 6). The dotted,
double-headed arrows represent the mutual influence between the earliest steps in the process—seeking information about one or more dating sites(Step 1) and registering for one or more of them (Step 2)—and the browsing of profiles (Step 4).The dashed, single-headed arrows represent feedback
loops that can emerge when users remain invested in finding a partner through online dating despite experiencing some amount of dissatisfaction with
their current circumstances at a certain step in the process. Users can drop out of the online dating process altogether at any stage, although we do
not depict this dropout process in the figure. ODS = online dating site.

everyday life who could potentially become a romantic partner: (a) by approaching the stranger directly in person (e.g., ata coffee shop), (b) by having a member of their social network
introduce them in person (e.g., at a party), and (c) by having amember of their social network set them up for a blind date to
be coordinated by the daters themselves. As this section illustrates, Steps 1 through 6 in the online dating process are largely
irrelevant to, or at least markedly different from, the process
for these more traditional means of meeting a potential partner, although the motives underlying these steps (e.g., to datedesirable partners) frequently have analogs in offline dating.
Step 7 (mutual mediated communication) might on rare occasions precede Step 8 (meeting face-to-face) in offline forms ofdating, such as when potential partners use e-mail to arrange
an initial meeting location for a blind date. Once online daters
reach Steps 8 and 9, the process becomes increasingly similar
to that experienced through conventional means of dating, atleast cosmetically; scholars have yet to determine whether
relationship development and maintenance processes differ
depending upon whether people met online versus offline (orwhether such processes differ depending whether online daters met through one dating site versus another).
Step 1: Seek information about one or more dating sites. Online
dating provides an expedient way to learn about potential partners and eliminate those who seem unappealing (Henry-Waring & Barraket, 2008; Whitty & Carr, 2006). In the words of
one online dater, “Where else can you go in a matter of 20
minutes, look at 200 women who are single and want to go on
dates?” (quoted in Heino, Ellison, & Gibbs, 2010, p. 438).

Many factors predict whether people are likely to seek
information about dating sites, some of which are similar to
those that predict whether people are likely to seek information about ways to meet potential partners offline. The twostrongest predictors of engaging in online dating are being an
Internet user and being single (Sautter et al., 2010). In addition, people are especially likely to seek information about
online dating if they are having difficulty finding appealing
potential romantic partners. Many people confront such difficulty, particularly if they lack established social networks or if
their social encounters are largely saturated with people whoare not suitable romantic partners for them. Indeed, online dating is especially prevalent among people who have a minority
sexual orientation (Hogan et al., 2011; Rosenfeld, 2010); whohave recently moved to a new area or experienced a breakup(Yurchisin, Watchravesringkan, & McCabe, 2005); who are
middle-aged rather than young-adult (G. Gonzaga, 2011;
Hogan et al., 2011; Rosenfeld, 2010; Stephure, Boon, MacKinnon, & Deveau, 2009; Valkenburg & Peter, 2007; Whitty &Buchanan, 2009); who are divorced rather than never-married(Sautter et al., 2010); who have limited time to meet potential
partners because of factors such as working long hours or
being a single parent (Barraket & Henry-Waring, 2008); or
who have lost interest in the bar scene and lack insight into or
options for where they can meet singles, especially appealing
singles, in their age range (Long, 2010). Furthermore, people
are frequently inspired to date online if they learn about friends
or family members having positive experiences with online
dating (Long, 2010).

Online Dating
Scholars have examined the personality characteristics ofpeople who date online and people who do not, discoveringthat the two groups tend to be much more similar than different. They are approximately equal in terms of self-esteem andthe Big Five personality dimensions (Aretz, Demuth, Schmidt,& Vierlein, 2010; Steffek & Loving, 2009). The literatureoffers conflicting evidence about whether online daters are
more versus less socially skilled than people who do not dateonline (Aretz et al., 2010; Kim, Kwon, & Lee, 2009; Valkenburg & Peter, 2007; Whitty & Buchanan, 2009); overall, there
do not appear to be important main-effect differences on thisdimension. Even when the two groups differ (e.g., online daters tend to be somewhat less religious, less likely to endorse
traditional gender roles, and more likely to say that they try
new things; Madden & Lenhart, 2006), these differences tend
not to be large.
Step 2: Register for one or more dating sites. Once people
have gathered information about dating sites, they can either
register with one or more of them or decide not to pursue apartner online. If they decide to register, how do they decide
which dating site (or sites) to join? One consideration is themanner in which the dating site presents users with profilesof potential partners—through self-selection or algorithmselection or through some combination of the two. A second
factor people consider is financial cost. As noted previously,some dating sites, including PlentyOfFish and OkCupid, arefree. Many other dating sites, however, charge fees. For example, in 2011, Match charged $35 for a 1-month membership,$60 for a 3-month membership, and $102 for a 6-month membership; eHarmony charged $60 for a 1-month membership,$120 for a 3-month membership, and $180 for a 6-month
membership (LittleRedRails.com, 2011a; LittleRedRails.com,
2011b). A third factor people consider is the degree to which
the brand or culture of a given dating site dovetails with theirgoals. Proprietors of dating sites work hard to cultivate theirbrands. As in more conventional dating contexts, where certain people might prefer to meet somebody through a religiousorganization rather than at a dance club or vice versa, online
daters might prefer the culture or brand at some sites morethan others. For example, because of its ambitious advertising
campaign, its clear emphasis on long-term compatibility, andits relatively high user fees, eHarmony tends to be especiallyappealing to people interested in forming serious, long-term
relationships (Long, 2010). In contrast, because of its particularly strong emphasis on physical appearance, fun, and its
absence of user fees, PlentyOfFish frequently draws people
who are interested in casual dating (Long, 2010).
Ultimately, users’ decisions about which dating site (orsites) to join involve a cost/benefit analysis. After gathering
information about online dating, people decide whether joining a given dating site is likely to yield sufficient benefits—
including efficiency, access to appealing potential partners,
and the potential to form a strong relationship—to outweighthe costs—including money, time, and the potential awkwardness of having one’s dating profile available to strangers.

15
Step 3: Create a profile on one or more dating sites—and,where relevant, complete a matching questionnaire. A user’s firststep after joining a dating site is to create and post a profile,after which potential partners can contact the user immediately. The moment at which a user posts a profile is the point
at which the romantic acquaintance process in online dating
starts to diverge fundamentally from the romantic acquaintance process in conventional offline dating. In conventional
offline dating, an individual cannot create a finely crafted synopsis of him- or herself that potential partners can access at
any hour of the day, even when its creator is sleeping.
In online dating, an individual creates such a synopsis in
the form of a profile, which can be updated at any time andwhich functions as one’s “own personal ƒshop window’”(Henry-Waring & Barraket, 2008, p. 21). It typically includes
one or more photos of oneself, basic demographic information(e.g., age, sex, education, profession, number of children, geographical location, religion, relationship status, sexual orientation), information about behaviors and interests (e.g., smoking
and drinking behavior, hobbies), and descriptions of the characteristics sought in a potential partner. Dating sites generally
encourage users to post at least one photo, and with good reason: Users who do not post photos are much less likely to becontacted (e.g., Fiore, Taylor, Mendelsohn, & Hearst, 2008).
The information users present in their profiles tends not to
be entirely veridical. One reason for this tendency is that people generally lack perfectly accurate self-perceptions (Ellison,
Heino, & Gibbs, 2006). Perhaps a more common reason, however, is that people engage in strategic self-presentation whenconstructing their profiles (Ellison et al., 2006; Whitty, 2008),
just as they do in traditional dating. One study analyzed the
profiles of 21,745 users at a dating site and compared the data
presented in the profiles with data of national averages for thesame characteristics (Hitsch, Hortaçsu, & Ariely, 2010a). Men
and women at the dating sites were slightly taller than theobjective national averages (1.3 inches for men and 1.0 inch
for women). Women reported weighing from 5.4 pounds less(the 20–29-year-old age group) to 22.9 pounds less (the50–59-year-old age group) than the national averages; men’sreported weight generally aligned closely with the national
averages. These comparisons to national norms are suggestiveof biased reporting on profiles, but more direct evidence
comes from a study comparing the profiles of 80 online daters
to their actual characteristics (Toma, Hancock, & Ellison,2008). When the researchers assessed height with a tape measure, weight with a scale, and age as reported on an individual’s driver’s license, they discovered that 81% of these online
daters reported inaccurate information on at least one of thesethree characteristics on their profile; about 60% lied about
their weight, 48% about their height, and 19% about their age.
This sample, however, was relatively young (inaccurate reporting about age would perhaps be greater for an older sample).
Men tended to overestimate their height and women tended tounderestimate their weight. Generally, the degree of deception
was small and would be difficult to detect face-to-face.

16
At dating sites that offer matching, users provide additional
information that is used for matching rather than for the profileand that potential partners cannot see. Some dating sites use aquestionnaire to asses this matching information. Responses
on this questionnaire can be crucial because strict matchingsites only allow users to view profiles of potential partners
whom the algorithm declares, based on the responses, to be amatch.
Step 4: Browse others’ profiles (optional). In conventional
offline dating, people have various forms of access to potential
partners, sometimes even learning bits of information about
them before meeting them in person, but people do not haveaccess to finely crafted synopses of potential partners to help
make the decision whether they would be interested in meetingthem in person. In contrast, many online dating sites offer precisely such information at any hour of the day. Sites primarilyemphasizing self-selection typically allow users to browse theprofiles of all the potential partners fitting their search criteria(e.g., heterosexual men between 35 and 50 years old who live
within 10 miles of a certain zip code, who do not have children,
and who are nonsmokers). In urban areas, popular selfselection dating sites frequently present users with access to the
profiles of thousands of potential partners, depending upon thebreadth of the search criteria (e.g., within 25 vs. 10 miles of acertain zip code, with vs. without smoking-status restrictions).
The logic underlying self-selection sites is that shopping for aromantic partner has crucial features in common with shopping
for goods or services. Such sites allow users to browse all of theavailable profiles with the expectation that they can later contact those potential partners whose profiles they find mostenticing. The process of browsing through hundreds, eventhousands, of profiles on self-selection sites can be timeconsuming, however. For example, a sample of users of one(unnamed) self-selection site reported that they spent an average of 5.2 hours per week browsing profiles (and another6.7 hours writing and responding to e-mails), a process they did
not particularly enjoy and that yielded only 1.8 hours of faceto-face interaction (Frost et al., 2008).
In contrast to sites emphasizing self-selection, sites emphasizing algorithm selection (i.e., matching sites) typically allow
users to browse only the profiles of potential partners whom
the site’s matching algorithm has deemed compatible withthem. Such sites frequently provide only a handful of profiles
at a time, although they typically contact users periodicallywith additional matches. The logic underlying algorithmselection sites is that users may lack the ability or motivation
to evaluate which partners are especially compatible withthem and that the algorithm can increase the odds that they
will meet such partners.
Browsing others’ profiles is a central activity—perhaps thecentral activity—in online dating, although it plays a smallerrole at matching sites than it does at self-selection or hybridsites. Indeed, browsing profiles is the primary means throughwhich dating sites offer access; the opportunity to browse profiles is one of the three major services of online dating (and the

Finkel et al.
profiles to which users have access are sometimes determinedthrough an algorithm-based matching process, another of thethree major services). In the online dating process depictedwith the solid arrows in Figure 2, a user browses others’ profiles (Step 4) after creating his or her own profile (Step 3).
However, the user can also arrive at profile browsing from anyother step in the process. Users are particularly likely to return
to browsing profiles from Steps 5 through 9 if they remain
invested in finding a partner through a given dating site despite
experiencing some amount of dissatisfaction with their current
level of success at those later steps.
At both self-selection and matching sites, the metaphorof shopping characterizes how users select which potential
partners are interesting enough to make it past the profilebrowsing stage. Indeed, Heino and colleagues (2010) conceptualized online dating as “relationshopping” (also see Whitty& Carr, 2006), suggesting that it can result in the objectification of potential partner (a topic upon which we elaborate
below). Participants in that study tended to view others’ profiles as sales pitches or promotional devices in which the
product others were selling was themselves, and they frequently became suspicious that others had embellished theirprofiles.
The shopping mentality seems to emerge in part from having immediate access to the profiles representing all of these“products” (potential partners). One online dater illustrated
this mentality as follows: “You know, ƒI’ll take her, her, her’—
like out of a catalog” (quoted in Heino et al., 2010, p. 437). A
second online dater agreed (quoted in Long, 2010, p. 206):
I think [shopping is] a perfect analogy for it. I can pickand choose; I can choose what size I want, it’s like buying a car, what options am I looking for. I can test drive
it, eh it’s not really my fit, I’ll put it back and go try
another car. . . . You might say I only want to look forredheads today, so I’ll save the search where all myother criteria are the same, education, professional, but
I only want to look for people who have red hair.
Of course, you can surely buy a car from a dealer if you
are willing to spend enough money, whereas you can only
procure a redhead from a dating site if she wants you in
return.
Although the shopping metaphor also applies to traditional
forms of dating, it reaches new heights in online dating, especially at self-selection dating sites. This mentality emerges inpart because of the sheer quantity of potential partners thatusers can evaluate and in part because of the manner in which
they do so. In particular, online dating typically requires thatpeople seek partners on the basis of searchable attributes likeincome and religion rather than experiential attributes likesense of humor and rapport (Frost et al., 2008). In the words of
one online dater, “You’re constantly evaluating as opposed tomeeting someone and not knowing anything about them but
knowing there’s already a spark” (quoted in Heino et al., 2010,

Online Datingp. 437). Thus, despite the tendency for profiles to convey asignificant amount of information, sometimes deeply personal
information, most romantic relationships will not begin until aface-to-face interaction takes place.
Step 5: Initiate contact through the dating site (optional). Steps
5 through 7 are relevant to the communication service offeredthrough online dating, the last of the three key services (accessand matching were most relevant to Step 4). As in traditional
dating, users’ decisions regarding whether to contact potential
partners are not random. For example, users reported that they
were especially likely to initiate contact with potential partners whose profiles made them appear to be physically attractive, to have similar interests and values to the users, and to bein the same general age and educational attainment ranges as
the users (Rosen, Cheever, Cummings, & Felt, 2008; Skopek,
Schulz, & Blossfeld, 2011; Whitty, 2008).
These self-reports were corroborated and extended by animpressive study of the profile-searching and contact-initiation
behavior of 6,485 heterosexual users of a major self-selection
dating site in the United States (3,702 men, 2,783 women;
Hitsch, Hortaçsu, & Ariely, 2010b), a subset of the 21,745
online daters whose height and weight were compared tonational averages in a separate article we summarized earlier(Hitsch et al., 2010a). This study examined the likelihood thatusers would contact a potential partner after viewing his or her
profile. Men viewed more than three times as many profiles as
women did (597,169 vs. 196,363) and were approximately
40% more likely to initiate contact with a woman after viewing
her profile than women were after viewing a man’s profile(12.5% vs. 9.0%); the average man sent 3.2 times more firstcontact e-mails than did the average woman over a 3.5-month
period (M = 20.1 vs. 6.3). Users of both sexes were especiallylikely to initiate contact with potential partners who were rated
by objective observers as physically attractive and who claimed
to earn a higher income. They also tended to contact partners
who were similar to themselves in terms of characteristics likerace, religion, political orientation, educational achievement,
single versus divorced status, parenthood status, and smoking
status. Finally, although online daters sometimes report thatthey do not pursue the most attractive potential partners (e.g.,
Heino et al., 2010; Long, 2010), Hitsch and colleagues’ (2010b)
data suggest that online daters typically pursue particularly
desirable potential partners rather than partners who matchthemselves on social desirability; that is, they shoot for thestars (but see Lee, Loewenstein, Ariely, Hong, & Young, 2008).
If a user elects to initiate contact with a potential partner,
this process can be similar to contacting a potential partner via
e-mail in conventional offline dating, except that the user has
not met the potential partner and no mutual acquaintance has
provided the contact information. Aside from these differences, dating sites frequently offer various means of initiating
contact other than sending an e-mail-type message. The
mechanics of the contact initiation process vary from one dating site to the next (Long, 2010). For example, users of Matchcan send a virtual wink, a pre-written message, or an open

17
message they draft themselves. There is no restriction or guidance regarding the communication process. PlentyOfFish iseven more freewheeling; users simply send a self-drafted message. In contrast, the communication process at eHarmony isremarkably lengthy and regimented, which makes the initiation process diverge even more sharply from the initiation process in conventional offline dating.
Two additional features of the contact-initiation process
warrant comment. First, dating sites that require users to pay
for their services typically allow users to browse profiles andinitiate rudimentary contact with potential partners without
paying but require payment for more elaborate communication(Long, 2010). On Match, for example, users can send a wink
without paying, but they must pay to send a message. On eHarmony, users can see their matches’ profiles (without photographs) and exchange basic closed-ended messages without
paying, but they must pay to see the photographs or to participate in the rest of the communication process. Second, at manyfee-based dating sites, potential partners with active profiles
frequently are not paid-up members, which means their ability
to reply to interested users is heavily restricted unless they subscribe (or resubscribe). Dating sites appear to have an incentive
to entice users to send such messages because it can help convert nonpaying members to paying members (Rudder, 2010).
Step 6: Receive contact through the dating site. As noted previously and illustrated in Figure 2, a user can receive contactfrom potential partners either (a) by browsing others’ profiles(Step 4), initiating contact with one or more of them (Step 5),
and receiving one or more replies through the dating site (Step6); or (b) by receiving unsolicited contact from one or morepotential partners through the dating site (Step 6). Users varyin the degree to which they favor the more assertive path or the
more passive path. Compared to women, men are more likely
to favor the assertive path over the passive path (e.g., Hitsch
et al., 2010b). The behavior of a 43 year-old female online
dater exemplifies the passive path:
I don’t browse profiles. . . . I sit around and wait, if
somebody winks at me, I look and see if I’m interested.
If I’m intrigued at all by their profile, I’ll wink back andwe’ll start to chat (quoted in Long, 2010, p. 203).
Some users at self-selection dating sites, particularly
women with attractive profile photos, can be deluged with
first-contacts (Hitsch et al., 2010b). Long (2010, pp. 130, 138)
reports that,
Within the first 20 minutes of creating my profile [onPlentyOfFish] I received four messages. . . . On Matchor POF [PlentyOfFish], a date seeker may log onto thesite to find 40 new winks or 20 new messages and 50
people who have viewed their profile.
Although it might seem appealing to have so many potential
partners clamoring for one’s romantic attention, this deluge

18
can cause users to disengage from the process rather than revelin it.
An ambitious recent study examined which contact initiation messages (Step 5) were especially likely to elicit a reply(Step 6; Schöndienst & Dang-Xuan, 2011). A linguistic analysis of 167,276 contact-initiation messages sent by 3,657 online
daters revealed that such messages were more likely to receivea response to the degree they were characterized by lower
usage of the pronoun I, lower usage of leisure words likemovie, higher usage of the pronoun you, and higher usage ofsocial-process words like relationship and helpful. Perhaps
surprisingly, usage of negative emotion words did not predictthe likelihood of a response.
Step 7: Engage in mutual mediated communication. Given
that, in conventional offline dating, one almost never receives
CMC from a stranger who lacks any ties with one’s social network, traditional social norms are not optimally tailored toonline dating. Simply ignoring an unwanted date request typically would be considered rude in a face-to-face context, but
the relative anonymity at this stage of the online dating process renders it a common and frequently inoffensive strategy,
along with the use of impersonal, scripted “no thanks” messages generated by the site (Tong & Walther, 2011). Indeed,
the overall reply rate tends to be somewhat low, perhaps inpart because of the potential for the initial message deluge andthe large number of non-paid-up members. In one recent study,
men replied to 26% of the messages they received through thesite, and women replied to 16% (Fiore, Taylor, Xhong, Mendelsohn, & Cheshirep, 2010; also see Rosen et al., 2008). This
study also found little evidence that eager responses were aturnoff; the faster the reply, the more likely that reciprocal
communication would continue.
If a user elects to respond to a contact-initiation attemptfrom a potential partner, he or she must decide upon the natureof that response, which can be heavily influenced by the features of the given dating site. For example, on Match, users
who receive a wink can respond with a wink, a low-effort
approach that is likely to convey lukewarm interest. Alternatively, users can, for example, respond with a personalized
message demonstrating that they have perused the potential
partner’s profile and have prioritized giving a personalized
response.
More generally, there is a range of mechanisms for communicating through dating sites. For example, two individuals
can communicate with each other through e-mail-like messages, instant messaging, webcam conversations, and evenvirtual dates with avatars, with the specific mechanisms available varying from one dating site to the next. During this stage,users must decide whether to provide personal contact information, such as e-mail addresses or phone numbers. Users
who have concerns about safety issues are especially likely toseek additional information about potential partners before
divulging much personal information (Gibbs, Ellison, & Lai,
2011).

Finkel et al.
Although users can go directly from dating-site-mediatedcommunication to a face-to-face meeting (e.g., by schedulinga date through the dating site instant messaging system), mostusers engage in communication outside of the dating site, frequently via personal e-mail accounts, before deciding to meetface-to-face (Day, Hamilton, Hutchins, Maher, & Vance,2010).5 Most of the dyads that progress to the mutual mediatedcommunication stage meet face-to-face within a month, frequently within a week (Rosen et al., 2008; Whitty, 2008).
Step 8: Meet face-to-face. Once two people start engaging inmutual mediated communication outside of the dating site, thedating site frequently becomes irrelevant to the nascent relationship, and it becomes increasingly irrelevant once theymeet face-to-face (Whitty & Carr, 2006). Users frequently
approach this first date as a screening process rather than as anoccasion for a deep romantic connection: “The first meeting
was essentially a test to see if there would be a second date”(Long, 2010, p. 238; also see Whitty, 2008; Whitty & Carr,
2006). One important issue is that people (especially women;
Gibbs et al., 2011) frequently experience stronger concerns
about physical safety issues on a first date when they have met
the person through online dating rather than through conventional offline dating. To address these concerns, people typically have their first date in a public place, such as a coffee
shop or a restaurant, rather than in a private home. At thispoint, the myriad factors that predict attraction and relationship initiation in face-to-face settings are surely relevant (forreviews, see Finkel & Baumeister, 2010; Finkel & Eastwick,in press)—factors including familiarity (Reis, Maniaci, Caprariello, Eastwick, & Finkel, 2011a; Reis, Maniaci, Caprariello, Eastwick, & Finkel, 2011b), reciprocal liking (Eastwick,Finkel, Mochon, & Ariely, 2007), and conversation quality(Sprecher & Duck, 1994).
Step 9: Develop an offline relationship. After the first date,each person decides whether he or she would like to go on asecond date. If at least one of the two people is not interested,
the relationship typically ends, and each person pursues one ormore of three paths: (a) browsing additional profiles on a dating site one has already joined (Step 4 in fig 2), (b) seeking
information about other dating sites (Step 1) and perhaps joining one or more of them (Step 2), or (c) discontinuing online
dating. If both partners are interested in pursuing the relationship further, they typically schedule a second date and perhaps
additional dates thereafter. If a relationship starts to become
more serious, each person decides whether to keep his or her
online dating profile active or to remove it. This decision
involves a range of considerations, including the conflicting
desires to convey a certain level of interest in this particularpartner and to continue exploring potential alternative partners (Long, 2010). As noted previously, scholars have not
performed systematic analyses to determine the degree towhich relationship development and maintenance processes
differ depending upon whether people met online versus
offline.

Online Dating
The romantic acquaintance process—conclusion. Thissection explained how the online dating process works andcontrasted this process with conventional offline dating.
Although online dating presumably has not altered the natureof intimacy, it has changed the mechanics of relationshipinitiation in fundamental ways. For example, rather thansharing information about oneself with potential partners inreal time, potential partners can browse one’s profile at any
time without one’s awareness. Rather than meeting potential
partners and then slowly learning various facts about them,users of online dating sites typically learn a broad range offacts about potential partners (and vice versa) before deciding
whether a first meeting is desirable. Rather than periodicallylearning about one or two potential partners who could beinterested in meeting people to date, users of dating sites,
especially users of self-selection sites, can learn about
hundreds or thousands of potential partners within hours.
This summary illustrates some of the fundamental ways inwhich the acquaintance process—learning about potential
romantic partners and perhaps initiating a relationship withthem—differs between online dating and conventional offline
dating. As discussed in Part II of this article, the most significant differences involve the ways people (a) access potential
partners, (b) communicate with these partners, and (c) get
matched with potentially compatible partners by third parties,
which are the main services that singles look to online datingsites to perform.
Before concluding this section, we address a final question:
How do online daters like online dating? In general, they
appear to find online dating and offline dating to be equallysatisfying. In one study of 759 online daters from in andaround Los Angeles, CA (47% male, 53% female), participants rated their experience with both types of dating as comparably positive (76% rated online dating positively), and 73%of them would recommend online dating to other people(Rosen et al., 2008).
In short, not only has online dating rapidly become a pervasive means through which singles seek to meet potential
romantic partners, but it has fundamentally altered the acquaintance process. In the third and final section of Part I, we examine whether online dating has fundamentally altered theprocess of compatibility matching.

Has online dating fundamentally altered theprocess of compatibility matching?
As observed at the outset of this article, third parties—religious leaders, parents, elderly women, and so forth—havesought to match compatible partners for many centuries. However, they generally worked on a small scale, introducing
potential partners whom they knew personally. Furthermore,they relied primarily upon their intuition when identifying
compatible matches. Our analysis will suggest that third-party
compatibility matching began to change fundamentally with19
the emergence of algorithm-based matching sites in the year
2000.
We illustrate algorithm-based matching by providing an indepth analysis of the approaches taken by three prominentsites that claim their matching process is scientific: eHarmony,
PerfectMatch, and Chemistry. Our examples draw most heavily from eHarmony, which is in a league of its own because (a)it was the first of the new generation of matching sites to
employ claims of scientific validity; (b) it has the greatest
media presence, with television advertisements, other forms of
media, and Internet blogs highlighting its matching system;and (c) it has patented its matching system (Buckwalter,
Carter, Forgatch, Parsons, & Warren, 2004; Buckwalter,
Carter, Forgatch, Parsons, & Warren, 2008).
Matching sites frequently claim, boldly and explicitly, thatthe key to relationship success is finding the perfect partner
and that they offer the optimal procedure to help users findsuch a partner. For example, eHarmony states, “Out of all of
the single people you will meet in your life, only a very few
would make a great relationship partner for you—by combining the best scientific research with detailed profiling of every
member, we screen thousands of single men and single women
to bring you only the ones that have the potential to be truly
right for you” (eHarmony Scientific Matching Making, 2011).
The PerfectMatch Web site states that it offers the “bestapproach to finding your perfect match” (PerfectMatch.com,
2011a, para. 1). Although Chemistry’s Web site emphasizes
finding a partner with whom one has “chemistry,” Helen
Fisher, the scientific advisor for Chemistry, emphasizes using
science to find one’s soulmate (e.g., Fisher, 2011).
The industry buzz phrase for the procedures used to generate compatible matches for members is matching algorithms(e.g., Gelles, 2011; Mitchell, 2009; Orenstein, 2003; Tierney,2008). Dating sites often assert that their algorithms are proprietary and cannot be revealed, presumably because the sites
would lose an advantage over competitors. For example, Gian
Gonzaga, a psychological scientist and the chief scientist at
eHarmony, has stated that eHarmony will not submit its matching algorithms to outside inspection because it would “reveal
the site’s ‘secret sauce’ to competitors” (as quoted in The
Economist, 2011, para 21). Although we lack insider information about the specific format of the algorithms used by thesesites, we can offer reasonably informed overviews by drawing
upon information in the public domain, including the company
Web sites; media interviews conducted with the sites’ chief
executive officers, staff, and creators of the matching systems;
and eHarmony’s patent.
Regardless of the specific implementation of a matchingalgorithm, the creators would have to consider at least three
general issues. First, what type of data should be collectedfrom users—what variables should be measured, and how?
Second, how should the data collected from a specific user be
combined with the data collected from potential partners todetermine the most compatible matches? Third, to what

20
degree, if at all, does the site consider the “objective” desirability of a given individual as a relationship partner in general(rather than with regard to any specific partner)? All of thesequestions subsume several additional issues, including how
responses to survey items are combined for total composite
scores, whether some variables are weighted more heavily
than others in the creation of compatibility scores or in thematching procedure, and whether any non-self-report data areincorporated into the matching algorithm.
Variables emphasized in compatibility matching. The threematching sites we review in detail all require that users complete surveys before they can be matched (see Table 2). The
matching sites’ Web pages (and other sources) refer to the variables or dimensions that are measured by the surveys and usedin the sites’ matching systems (see the “Dimensions of Variables” column in Table 2). The eHarmony matching system andsurvey were originally developed by a team that included thefounder, Neil Clark Warren, a clinician, and Galen Buckwalter,a psychological scientist. Decisions about which variables tomeasure were made based on interviews Warren conducted withmarried individuals (Peel, 2008; Warren, 2002) and a review of“the psychological literature to identify the areas that might berelevant in predicting success in long-term relationships” (asreported in Gottlieb, 2006). Initial psychometric tests were performed on a large sample of couples, although the details of thispilot study are not publicly available.
The current version of the eHarmony survey has 13 sections and approximately 300 items. The survey is designed tomeasure 29 dimensions, which eHarmony claims are predictive of long-term relationship success (e.g., Warren, 2002). On
the eHarmony Web site (eHarmony Scientific Match Making,2011), these 29 dimensions are grouped into higher-order categories and further distinguished as either core traits, whicheHarmony suggests are unlikely to change in adulthood (seefirst four rows in Table 2) or vital attributes, which eHarmony
suggests may change based on learning and experience (seenext three rows in Table 2).
The survey presents personality adjectives (e.g., warm,competitive), interests (e.g., volunteering, keeping physicallyfit), and emotions (happy, angry) and asks users to indicate
how well each adjective describes them. Members also
respond to self-descriptive statements (e.g., “I have an ability
to make others laugh,” “I am looking for a long-term relationship that will ultimately lead to marriage”). Information about
which items are combined to measure the particular dimensions or clusters of dimensions, or about the reliability andvalidity of the measures, is not publicly available.
The “Duet Total Compatibility System” at PerfectMatch
was developed in part by sociologist Pepper Schwartz. The
PerfectMatch survey contains slightly more than 100 items,
and is based loosely on the Myers-Briggs Theory and Type
Indicator, which was developed decades ago to help managers
determine which personality types may work well together ona team (P. Schwartz, 2006). The variables measured in

Finkel et al.
the PerfectMatch survey, which overlap only slightly with thepersonality types in the Myers-Briggs measure, focus on eight
personality characteristics (see the “Dimensions of Variables”
column in Table 2). P. Schwartz (2006, p. 10) claims that theDuet system “uses characteristics based on research from thesociology and psychology of love and compatibility.”
Each personality type is assessed with seven items, eachconsisting of two to three sentences or phrases. An example
item to measure romantic impulsivity is, “Almost every time I
have been in love I had an overall physical response when wemet; my heart beat fast, I got sweaty palms, I felt a bit hyper, I
was overly talkative, or became uncharacteristically shy.” Anexample item that measures self-nurturing is, “I share my feelings with my partner on my own schedule. I don’t feel comfortable if he/she always wants to know what I’m thinking or
feeling. It bothers me if a partner doesn’t respect my boundaries.” The survey also includes questions about money, sex,
children, core values, and social life. We are not aware of any
research conducted to facilitate the creation of the PerfectMatch survey or to establish the reliability or validity of thedimensions.
The Chemistry matching system and survey were developed by anthropologist Helen Fisher (Fisher, 2009). Fisher
proposes that there are four personality types and that thesetypes are linked to two sex hormones (testosterone and estrogen) and two neurotransmitters (dopamine and serotonin). As
presented in the “Dimensions of Variables” column of Table 2,these personality types are labeled Explorer (adventurous, creative, energetic), Builder (calm, cautious, conventional),
Director (dominant, analytical, decisive), and Negotiator(empathetic, idealistic, emotionally expressive). People areclassified according to a primary and a secondary type.
The survey has varied in length over time but currently
contains about 60 items and includes a question that asks about
the length of one’s index finger compared to the ring finger,
which Fisher argues is associated with level of testosterone(Fisher, Rich, Island, & Marchalik, 2010; also see Manning,2002). Users are also shown visual representations and asked
their interpretation (e.g., “Which of 4 faces have phony vs.
real smiles”), with different responses putatively corresponding to the four personality types. In addition, users indicate
their level of agreement with self-descriptive statements suchas “I am always looking for new experiences” (Explorer/
Dopamine), “It is important to respect authority” (Builder/
Serotonin), “I pursue intellectual topics thoroughly and regularly” (Director/Testosterone), and “I highly value deep emotional intimacy in my relationships” (Negotiator/Estrogen;
Fisher, 2009; Fisher, Rich, Island, & Marchalik, 2010). Fisher
has presented reliability information (e.g., Fisher, Rich, Island,
Marchalik et al., 2010), but validity information has been
scarce to nonexistent.
Although eHarmony, PerfectMatch, and Chemistry have long
built their matching algorithms upon self-report data, matchingsystems are increasingly taking behavioral variables into account(Humphries, 2010). For example, software used at eHarmony

Online Dating

21

Table 2. The Variables Measured at the Three Major Scientific Matching Sites and Matching Principles Used
Matching site

DomainConstruct

Variables assessed

Matching principle

eHarmonyCore traits: “defining aspects of
who you are that remain largely
unchanged throughout your adultlife”aEmotional
temperament

Self-concept, emotional status, energy,
passionSimilarity

Social style

Character, kindness, dominance,sociability, autonomy, adaptability
Intellect, curiosity, humor, artistic
passionEnergy, passion, vitality and security,
industry, appearance
Communication style, emotion
management, conflict resolution

Similarity

Cognitive mode
Physicality
Vital Attributes: “based on learning
experience, and are more likely tochange based on life events anddecisions you make as an adult”aRelationship skills

Values and beliefs
Key experiences
PerfectMatch

Chemistry

Sex hormones
Neurotransmitters

Testosterone
Estrogen
Dopamine
Serotonin

Spirituality, family goals, traditionalism,
ambition, altruism
Family background, family status,
education
Romantic impulsivity, Personal energy,
Outlook, Predictability
Flexibility, Decision-making style,
Emotionality, Self-nurturing
Director personality type
Negotiator personality type
Explorer personality type
Builder personality type

Similarity
Similarity
Similarity

Similarity
Similarity
Similarity
Similarity orcomplementarity
Complementarity
Complementarity
Similarity
Similaritya

From www.eharmony.com/why/dimensions

under chief technology officer Joseph Essas is similar to thetechnology used by Netflix and Google (Shambora, 2010):
Before Essas’s arrival 2 years ago, eHarmony analyzed
a few dozen variables per user. Today, its technology
takes into account hundreds of different traits, including
crucial information about how users behave—like timespent on the site or how long they take to respond to an
e-mail about a match. The company collects this behavioral data and uses it to predict how users will respondto proposed matches.
Indeed, eHarmony’s “matchmaking software gathers 600 data
points for each user, including how often they log in, who they
search for, and what characteristics are shared by the people
they actually contact” (Humphries, 2010, para. 4). It seems
that such sophisticated algorithms are also used at sites, like
PlentyOfFish and Match, that offer matching recommendations to supplement self-selections (Brooks, 2006).
In short, the major matching sites rely heavily on self-reportdata, focusing on a broad range of variables (see Table 2).
Many of these variables, including family background, spirituality, and emotionality, presumably overlap considerably withthe variables that third-party matchmakers have used for centuries. Other variables, however, represent a fundamental shift

from traditional compatibility matching. For example, it is difficult to imagine that rabbis in the 17th century sought toenhance matching compatibility by examining the length of
singles’ 2nd and 4th fingers to learn about their levels of testosterone. In addition, although third-party matchmakers
almost certainly took account of singles’ behaviors (rather
than just their self-reports) when searching for a match forthem, the types of behaviors that current matching sites use
were unavailable until recently. For example, matching algorithms can learn if a particular user tends to click on politicallyconservative blondes despite claiming to prefer politically liberal brunettes, and the algorithm will subsequently show thatuser more profiles of the former.
Scientific principles underlying compatibility matching.
The stated scientific principle behind the eHarmony Compatibility Matching System is similarity (see the last column inTable 2). According to Neil Clark Warren, eHarmony’s founder,“opposites attract, but then they attack” (quoted in Neate, 2009,
para 1). Warren (2002, p. 116) elaborates as follows:
The greater the differences between two people, thegreater the drain on their marital energy. So strongly do
I believe this that I have repeated over and over myadmonition: “For couples, similarities are like money in22

Finkel et al.
the bank, and differences are like debts they owe.”
Every difference requires an enormous amount of hard
work to manage, and this subtracts from the energy
needed to keep a marriage thriving.

Echoing these sentiments, eHarmony scientists Carter andBuckwalter (2009, p. 107) wrote that their online system is“accurately understood at a broad level to create pairings
based on a schema of maximizing the intra-dyad levels oftraits observed in empirical research to be positively related tomarriage quality, and minimizing intra-dyad differences on
traits where similarities have been observed to be positively
related to marriage quality.”
The stated scientific principles behind the Duet Total Compatibility System at PerfectMatch appear to be both similarity
and complementarity (see the last column in Table 2), andPepper Schwartz (2006), the site’s academic relationshipexpert and cocreator of the matching system, has argued thatboth are necessary for romantic compatibility. The PerfectMatch Web site states, “Unlike other matchmaking systems or
personality tests that match people based solely upon similar
character traits . . . Duet pioneered analyzing and matching
individuals who share both similar and complementary traits”(PerfectMatch.com, 2011b, para. 3). P. Schwartz (2006) has
stated that couples will be more compatible when they are
similar on romantic impulsivity, personal energy, outlook, andpredictability—and when they are different on flexibility,
decision-making style, emotionality, and self-nurturing.
Regarding flexibility, for example, P. Schwartz (2006)
argues that similarity works if both partners are flexible but
not if both partners are inflexible; pairing a highly inflexible
person with a highly flexible person works. Similarly, someone who has a dominant personality works better with someone who is willing to compromise than with another dominantperson. Schwartz also argues that although similarity on intensity/passion can work, as can similarity on being reserved, differences on emotionality can also be desirable because it canfoster growth and interdependence. Finally, she argues that
relationships work better when at least one partner is extroverted than when both partners are introverted.
Chemistry’s matching process focuses more on initialattraction than on long-term compatibility, and Fisher (2009)has argued that falling in love depends on both similarity andcomplementarity. For example, Fisher (2008, para. 9) argues(a) that “we are unconsciously attracted to those who complement ourselves biologically, as well as socially, psychologically, and intellectually”; and (b) that “we fall in love withsomeone who has a different chemical profile for dopamine,
serotonin, estrogen, and testosterone that complements ourown.” She also argues that individuals’ prominent neurochemical determines whether their preference is for similarity orcomplementarity; Explorers and Builders prefer similarity tothemselves, whereas Directors and Negotiators select one
another (Fisher, 2009).
In short, the primary scientific principle emphasized at the
major matching sites is similarity, although complementarity

also plays a prominent role at certain sites. Hybrid sites thatrecommend matches as a complement to their primary emphasis on self-selection (e.g., PlentyOfFish, OkCupid) alsoemphasize similarity (e.g., Brooks, 2006; Gelles, 2011).
Although the variables on which similarity and complementarity are assessed have changed since the advent of algorithmbased matching sites, these fundamental principles havepresumably been staples of third-party matching for
centuries.
Individual variation in matchability. Users of matchingsites are likely to vary in their “matchability,” which can beaffected by several factors, including the degree to which they
have characteristics that are likely to be generally attractive topotential partners, their mate standards (selectivity), and perhaps the number of potential partners in the users’ particulargeographical region. It appears that eHarmony classifies somepeople as poor prospects for a serious relationship and rejects
them from joining the site, even though doing so requires thatthe company forfeit the money they would have gained fromhaving such users as members. Media articles (e.g., Komblum,2005), including those based on interviews with Warren (eHarmony’s founder), suggest that people are rejected for being
married too many times, for responding to items in a way thatsuggests that they may have emotional problems such as
depression, and for being identified by the algorithm as providing misleading or inaccurate information on the survey.6 In
interviews, Warren also claims that people are rejected fromthe site when they are on the tail of distributions that makethem too desirable—too intelligent, too educated, etc.—
because of the difficulty of finding an appropriate match (Gottlieb, 2006). The unfortunate irony here is that eHarmony
appears to exclude the very people who are in the greatest needof a matching service: those who have the most difficulty finding a compatible partner. Although third-party matchmakers
presumably have long classified people according to theirlevel of general desirability, they presumably tried to matchparticularly undesirable people with people they determined tohave comparable levels of desirability.
Mathematical matching algorithms—conclusion. Mathematical matching algorithms make it possible to incorporate alevel of systematic complexity into the romantic matchingprocess that would have been impossible before the advent of
powerful computers. Such computational feats represent
another major distinction between conventional offline dating
and online dating: Although matching has existed for millennia, basing matching decisions upon scientific principles andsophisticated mathematical algorithms is a 21st-century
innovation.
In addition, although many variables relevant to algorithmbased matching are similar to those that matchmakers presumably have emphasized for millennia, some of the variables arequite new (e.g., Chemistry’s emphasis on neurotransmittersand hormones). The basic principles underlying matchingalgorithms appear to be similarity and complementarity,

Online Dating
principles that matchmakers presumably have also considered.
Although matchmakers have long used intuition or gossip toassess a single’s level of desirability as a partner, declaring
some people too desirable or too undesirable even to attempt
to match, as sites like eHarmony do, may have been rare inprevious eras, especially in the absence of knowing anybody
who had ever actually met the individual in question.

Part I—conclusion: Online dating isfundamentally new
Part I examined whether online dating is fundamentally different from conventional offline dating. Although there aremany similarities, there are also many fundamental differences. First, online dating has become pervasive, overshadowing many of the ways singles used to meet. Second, online
dating has fundamentally altered the romantic acquaintanceprocess. For example, millions of singles now first encounter
potential partners in the form of profiles rather than in theform of first-person meetings or first-hand accounts. In addition, these singles frequently have immediate access to (theprofiles of) hundreds or thousands of potential partners at any
hour of the day, and those potential partners have comparable
access to users’ own profiles. Furthermore, the means throughwhich singles initiate contact with potential partners is, inmany cases, fundamentally different from how they do so inconventional offline dating, especially regarding the various
forms of CMC that typically precede a face-to-face encounter.
Third, whereas both matchmakers and algorithm-basedmatching sites emphasize principles like similarity and complementarity when establishing compatibility, matching sites
frequently emphasize variables (e.g., serotonin) that matchmakers have historically ignored, and they seek to use vast
amounts of data to build their algorithms. In addition, certain
matching sites’ (e.g., eHarmony’s) decision to omit some people from the dating pool is presumably a more extreme
approach than conventional matchmakers would have pursued. In sum, online dating has fundamentally altered thedating landscape, especially vis-à-vis the forms of access,communication, and matching they offer to singles. We now
examine whether these changes have generally improved
people’s romantic outcomes.

Part II: Is Online Dating Superior To
Conventional Offline Dating?
The preceding discussions of prevalence, the mechanics of theonline dating process, and mathematical matching algorithms
converge upon the conclusion that online dating differs fundamentally from conventional offline dating (the uniquenessquestion). We now examine whether online dating fosters better romantic outcomes than conventional offline dating does(the superiority question). Toward that end, we critically evaluate dating sites’ claims of superiority before reviewing thescholarly literature relevant to evaluating whether specific23
implementations of access, communication, and matchingoffered by online dating are likely to yield superior romantic
outcomes.

Does the available evidence support online
dating sites’ claims of superiority
What claims of superiority do online dating sites make?
Perhaps not surprisingly, online dating sites’ claims of superiority are pervasive. We briefly review the general thrust ofthese claims, offering a handful of concrete examples, before
discussing the legal context for considering such claims andevaluating whether the evidence underlying them meets conventional standards of scientific validity.
Online dating sites make a broad range of explicit andimplicit claims about their access, communication, and matching services. Table 3 presents illustrative explicit claims, using
verbatim quotes from a range of online dating sites. By design,
the dating sites included in Table 3 represent a broad range of
methods and market share; for example, eHarmony has a large
market share, whereas Weopia does not. As illustrated in thetable, claims regarding access frequently involve information
about how many people use the site and how useful the profiles are for learning about potential partners. Claims regarding communication frequently involve information about whatforms of mediated communication the site offers and how
these forms of communication are especially effective andefficient at helping users (a) develop a nuanced view of what
potential partners are like and (b) discern whether those potential partners are likely to be compatible with them. Claims
regarding matching frequently provide a broad-brushstroke
analysis of how the site implements the matching process(e.g., personality matching, genetic matching) and why thismeans of implementation is likely to be effective.
News agencies frequently parrot these claims uncritically.
They often do so in awed tones, presenting the coverage as
news even though it sometimes more closely approximates aninfomercial for the dating site. For example, Good Morning
America (GMA), ABC’s popular morning television program,recently featured GenePartner, a dating site that claims tomatch potential partners based on their DNA. As part of thissegment (downloadable from GenePartner.com, 2011), GMA
interviewed Tamara Brown, co-founder of GenePartner, who
informed viewers that her company’s system of establishing“biological compatibility” is “really, really accurate.” Fortunately for her, GMA, with the ABC News logo featured prominently on the screen, was happy to spread the gospel,
introducing the segment as follows: “Now, hard science ismaking it easier to find true love. A new matchmaking system
uses DNA to help find your dream date, and it’s redefining
what it means to be compatible.” GMA elaborated: “But making that first match has always been an inexact science, kissing
a few frogs, unavoidable. Until now. With the use of DNA
technology, the science of online dating has become a whole
lot less inexact.” Despite a paucity of evidence that

24

Finkel et al.

Table 3. Quotes From Online Dating Sites Illustrating (a) the Means Through Which They Implement a Given Service (Access,Communication, or Matching), (b) Claims That Implementing the Service Through Those Means Is Successful, and (c) the Stated Theoretical
Rationale Underlying Those Means of Implementation
Function

Means of implementation

Claims of success

Underlying theory

AccessPlentyOfFish: “With over 145
Million monthly visitors
Plentyoffish is by far themarket leader in online dating.
You are not going to findany other site that has more
singles looking to meet new
people.”
JDate: “JDate has hundredsof thousands of members
worldwide and is one of the
few personals sites aroundthat boasts a nearly perfect
50:50 male-to-female ratio.”

PlentyOfFish: “the vast majority of singles
hear about us because their friends have
had a really good experience using ourdating service.”

PlentyOfFish: “Do you want to bewith someone? Do you want tofind your soulmate? Think about
it, where else are you going tofind millions of singles all in one
place looking for someone?”

JDate: “Each week, hundreds of JDaters
meet their soul mates.”

JDate: “You can also get to know
people and what they’re looking
for before you contact them,
thanks to detailed profiles withphotos, essays and all kinds of
personal tidbits.”

SpeedDate: “In under an hour on
SpeedDate, you can meet up
to 15 people using live video
and instant messaging.”

SpeedDate: “What differentiates our
product is our real-time dating system.
. . . We took the online profile model andthe super-matching algorithm model,
and took them one step further.”Weopia: “Weopia users select anavatar and meet their date in
an intimate person-to-person
3D virtual world. They use
voice and text chat while
engaging in dating activities
and conversation stimulators
to get a better sense of eachother.”

Weopia: “By allowing daters to meet invirtual worlds and to experience eachother beyond mere text chat, theircollective risk is reduced and theirlikelihood of success is increased.”

SpeedDate: “What made more
sense to us was to design an
online dating site where you
could tell quickly whether
you clicked or not, by goingon live online dates with othersingles from the comfort ofhome.”
Weopia: “It’s easy to fall for a
perfect profile picture and e-mailmessages that are rehearsed,
massaged and not spontaneous.
Weopia helps to bring out
the real person, quickly, in the
comfort and security of your own
home.”

eHarmony: “eHarmony’s
patented Compatibility
Matching System allows
eHarmony members to bematched with compatible
persons with whom they arelikely to enjoy a long-term
relationship.”
ScientificMatch: “Discover the
magic of chemistry withgenetic matchmaking. Our
private, secure, personalized
system will find you the mostperfect matches possible.”eHarmony: “On average, 542 people getmarried every day in the United States
because of eHarmony; that accounts fornearly 5% of new U.S. marriages.”

eHarmony: “eHarmony matches
singles based on a deeper level ofcompatibility, not likes and dislikes,
but true compatibility.”

ScientificMatch: “The 6 benefits of scientificmatching: (1) Chances are increased thatyou’ll love the natural body fragranceof your matches. (2) You have a greater
chance of a more satisfying sex life. (3)Women tend to enjoy a higher rate oforgasms with their partners. (4) Women
have a much lower chance of cheating intheir exclusive relationships. (5) Couples
tend to have higher rates of fertility. (6)All other things being equal, couples
have a greater chance of having healthierchildren with more robust immune
systems.”

ScientificMatch: “Physical chemistry
is based on the immune system.
When we analyze your DNA, welook exclusively at your immune
system genes. So, quite literally,
when we say that two people
have ‘chemistry’, we’re sayingthat their immune system genes
are perfectly matched with eachother.”

Communication

Matching

Note.The first column in the table clarifies which service a given quote illustrates (access, communication, or matching), the second presents the means throughwhich the sites implement that service, the third presents the sites’ claims of success, and the fourth presents the sites’ stated rationale for why implementingthat service through these means is likely to yield success.

Online Dating
DNA-based compatibility has an important influence onromantic outcomes (a topic upon which we elaborate later),
GMA functioned in this instance as a crucial component of
GenePartner’s public relations efforts. And, of course, GMA is
not alone, and GenePartner is not the only dating site covered
so effusively. Comparable segments pervade coverage of
online dating across the news media, which makes it especially important for psychological scientists to provide a critical evaluation of dating sites’ claims.
What is the legal context for considering online datingsites’ scientific claims? Dating sites’ claims do not exist in a
vacuum. The online dating industry is part of the broader business world, and governmental agencies have long regulated
what sorts of claims businesses are allowed to make. Many
dating sites, especially algorithm-based matching sites, claimthat their methods have been scientifically demonstrated toyield positive romantic outcomes. Some sites, including Matchand eHarmony, even report results from studies they have conducted or commissioned in support of their claims. However,these sites typically do not reveal the specifics of how theirprocedures work, especially vis-à-vis matching algorithms. As
elaborated below, disclosure of these specifics is necessary for
substantive evaluation of the validity of such claims. For
example, the general claim that a dating site matches potential
partners on the basis of the compatibility of their personality
or values cannot be evaluated meaningfully because it omits
information about which personality traits or values are considered and assessed, which are given greater or lesser weight,
and how compatibility is established.
We recognize that these procedures, especially the matching algorithms, represent the proprietary intellectual propertyof their firms, who are understandably reluctant to reveal theformulae that make their services distinctive and possibly
more effective relative to competitors. Secrecy of this sort isstandard in the for-profit sector. The formula for Coca-Cola isfamously clandestine, and Apple reveals the inner workings of
its operating systems only to software developers who sign
nondisclosure agreements. Nevertheless, firms like Coca-Colaand Apple rarely make explicit reference to a scientific basis
for their marketing claims, perhaps in part because the Federal
Trade Commission (FTC) Act
prohibits unfair or deceptive advertising in any medium.
. . . Advertising must tell the truth and not mislead consumers. . . . Claims must be substantiated, especiallywhen they concern health, safety, or performance. . . . If
your ad specifies a certain level of support for a claim—“tests show X”—you must have at least that level of
support (Bureau of Consumer Protection, 2011a, paras.
2–4).
Moreover, the FTC enjoins companies from using consumer testimonials (a common tactic used by online dating

25
sites in their advertisements) as a means of bypassing scientific substantiation:
By “competent and reliable scientific evidence,” theFTC means tests, studies or other research based on theexpertise of professionals in the field who have been
objectively conducted and evaluated by qualified peopleusing procedures that give accurate and reliable results.
. . . Claims made through consumer testimonials also
require scientific evidence. Some advertisers mistakenly
think they can get around the substantiation requirementby couching efficacy claims as consumer testimonials—“My arthritis pain vanished!” or “This product relieved
my allergy symptoms!” They’re wrong. Testimonials
aren’t “competent and reliable scientific evidence.” If
you don’t have solid science to prove the underlying
representation, don’t try to “back door” it through atestimonial. Furthermore, don’t forget that by using atestimonial, you’ve made an efficacy claim that has tobe substantiated” (Bureau of Consumer Protection,
2011b, paras. 2–3; emphasis in the original).
These and similar rules have fostered legal action when the
research does not actually support claimed benefits of a product(e.g., the 2007 Coca-Cola/Nestlé Enviga calorie-burning controversy, in which these firms were prohibited from making
weight-loss claims for certain products; Casewatch, 2011).
Indeed, at least one online dating firm has demanded that
another firm cease making claims it deems unsubstantiatedabout the number of dates and relationships it has produced andthe number of members it has (Gigaom, 2011). In several otherinstances, online dating firms have formally objected to one
another’s advertised claims of effectiveness through the selfregulatory program of the National Advertising Review Council, objections that the Council has upheld (NARC, 2011).
It may be instructive to draw an analogy to the pharmaceutical and medical-device industries, two other industries inwhich a product may have substantial implications for thehealth and well-being of its users (much as online dating may
have, as discussed earlier). These companies also assert thatthe benefits of their products are based on substantial scientific research. In fact, such evidence is a prerequisite forreceiving approval from the U.S. Food and Drug Administration (FDA), consistent with this organization’s responsibility
to protect the public’s welfare. Nevertheless, the sponsor of
an investigative drug or device must submit detailed information about its chemical or mechanical properties and theunderlying manufacturing process before being allowed to
conduct a study on humans. Furthermore, although the FDA
keeps certain proprietary information confidential, patentapplications, which are public documents, must disclose key
details. Thus, by the time a drug or device becomes available
to the public, key information about its central components isaccessible to scientists and other interested people. To our26
knowledge, no dating site has provided information of thissort to any regulatory agency.
Does the evidence underlying online dating sites’ claims
meet conventional standards of scientific validity? These
legal considerations notwithstanding, a crucial question iswhether dating sites’ claims are scientifically valid. An affirmative answer to this question depends, at minimum, upon thematching site (a) reporting the research methods and statisticalanalyses in sufficient detail to allow for independent replication and (b) adhering to consensually accepted standards forinterpreting data as free from artifact. To date, dating sites fallshort on both of these necessary criteria.
The impossibility of independent replication. Regarding thefirst criterion—reporting the research in sufficient detail toallow for independent replication—dating sites collect proprietary data and report whichever results they like in order tomake claims about the propriety procedures they use to introduce singles to one another. It is impossible for independentscholars to evaluate the validity of a given matching algorithm
because the matching sites refuse to share the algorithm withmembers of the scientific community. Such open sharing is
standard in science.
A related issue is that online dating sites rarely publish theirempirical findings in peer-reviewed reports. Indeed, we foundno published, peer-reviewed papers—or Internet postings, forthat matter—that explained in sufficient detail (that is, sufficient to allow determination of whether the data are free from
artifact or sufficient to permit replication) the criteria used bydating sites for matching or for selecting which profiles a user
gets to peruse.7 Scientific journals generally require this kindof information to evaluate a paper for publication. For example, the Publication Manual of the American Psychological
Association (American Psychological Association, 2001, p.
17) informs researchers that the Method section must describe“in detail how the study was conducted,” as “such a description enables the reader to evaluate the appropriateness of yourmethods and the reliability and the validity of your results”
and “permits experienced investigators to replicate the study ifthey so desire.”
Since at least 2004, scholars have repeatedly noted thenecessity of dating sites providing fully documented scientificstudies to support their various claims (e.g., Houran, Lange,
Rentfrow, & Bruckner, 2004; King, Austin-Oden, & Lohr,2009; M. Thompson, Zimbardo, & Hutchinson, 2005), but thesites have not adhered to these standards. Until the science
underlying the purported effectiveness of online dating protocols is subjected to the standard scrutiny, scientific claims
regarding the construction or effectiveness of such protocols
should be given little credence.
Failure to collect data that are free from artifact. Regarding
the second criterion—adherence to consensually accepted
standards for interpreting data as free from artifact—theempirical reports from online dating sites fall well short ofaccepted standards for establishing that any effects they might

Finkel et al.
find are due to the mechanisms they claim. For example, even
if a dating site shared sufficient information for scholars to be
confident that couples formed through that site were more satisfied than were couples formed through other means, the sitewould also have to show that any such differences were due tothe sites’ procedures rather than to a broad range of potential
confounds in the research. All dating sites function as datinginterventions, and there is a long tradition in the behavioral
and medical sciences of evaluating the impact of interventions. We discuss three issues in the online dating literature—
selection bias, expectancy effects, and the need for randomized
experiments—that converge upon the conclusion that theevidence dating sites have reported to date is susceptible totroubling artifacts.
i. Selection bias. Published research comparing couples who
met through a given dating site to those who met through someother means are compromised by selection bias, which refers
to a statistical artifact in which conclusions are distorted by theexistence of systematic differences between groups that are
unrelated to any variable the researchers are investigating. For
example, when eHarmony scholars reported that couples who
had met through eHarmony exhibited stronger marital adjustment than couples who had met through “unfettered selection”(Carter & Buckwalter, 2009), they failed to rule out a broad
range of plausible differences between the people in these twogroups that caused half of them to join eHarmony in the firstplace. People who seek to establish a romantic relationshiponline are not a random sample of single people who are unattached and interested in a relationship; instead, and minimally,
they tend to be people who are highly motivated to establish aromantic relationship. Such motivation may cause these people to evaluate with favoritism any company that offers accessto a large pool of potential partners or to be more likely tocommit to a relationship. In addition, people with the financial
resources and social-psychological skills to join a dating site
(especially eHarmony) and to pass the background screening
implemented by some sites (especially eHarmony) may bebetter candidates for successful relationships than otherpeople.
ii. Expectancy effects. A second issue plaguing research comparing couples who met through a given dating site to those
who met through some other means pertains to the possibilityof expectancy effects, which refer to the myriad ways that
research participants’ expectancies can bias a study’s results.
In particular, it seems plausible that users of a given matchingsite have some degree of faith in the matching algorithm’svalidity, and we suggest that this faith can cause them to experience positive romantic outcomes, at least in the short-term,that are entirely unrelated to the content of the algorithm.
Scholars have identified at least three interrelated processes
that may lend indirect support for this proposition.
One process, which resides at the intersection of research on
outcome dependency (Berscheid, Graziano, Monson, & Dermer,
1976) and research on the endowment effect and the status quo
bias (Kahneman, Knetsch, & Thaler, 1990; Kahneman, Knetsch,

Online Dating& Thaler, 1991; Samuelson & Zeckhauser, 1988; Thaler, 1980),
begins with the recognition that assigning users an algorithmselected match causes them to perceive that they have progressed through several early stages of the dating process,including narrowing the field of potential partners and finding
somebody with whom they might be especially compatible.
Given various pieces of evidence that the perception that one
has progressed through the early stages of a given processincreases one’s motivation to continue with that process (J. E.
Heyman, Orhun, & Ariely, 2004; Nunes & Dreze, 2006), a userof a dating site may be especially motivated to pursue a relationship with an algorithm-identified match.
A second process that may emerge as a result of users’ faith
in the validity of a site’s algorithm is the placebo effect, whichrefers to improvements in one’s psychological or physicalwell-being, or in one’s satisfaction with a particular experience, that result from the erroneous belief that one has received
an efficacious intervention (e.g., Plassman, O’Doherty, Shiv,& Rangel, 2008). A user’s belief in the validity of the algorithm used by a dating site may cause him or her to view amatch as compatible. That is, having a purportedly authoritative source claim to use science to select putatively ideal or
highly compatible matches could predispose people to be
more accepting of these matches, at least initially, than theymight otherwise be. Such a belief may increase their likelihood of contacting, and perhaps experiencing initial attraction
toward, him or her.
The third process is the confirmation bias, which refers totendency for people to seek or interpret evidence in ways thatare likely to support their existing beliefs or expectations (Nickerson, 1998; Snyder & Swann, 1978). Whereas the placebo
effect may cause users of matching sites to behave in accord
with the belief that selected potential partners are in fact compatible with them, the confirmation bias may help sustain thisbelief, at least in the short-term, by inspiring hypothesis tests
that are systematically biased toward confirming it. For example, a user might pursue certain lines of conversation with aselected potential partner that are especially likely to provide
evidence of compatibility (e.g., discussing known common
interests rather than more ambiguous topics).
iii. The need for randomized experiments. Selection bias andexpectancy effects are troubling methodological artifacts, butscholars can overcome them by employing optimal research
designs. Most researchers believe that randomized trials, inwhich participants are randomly assigned either to an intervention condition or to one or more suitable control conditions, are the gold standard for evaluating the effectiveness ofan intervention (Campbell & Stanley, 1963; Nezu & Nezu,
2008). Random assignment is crucial because it rules out various threats to internal validity (Campbell & Stanley, 1963;
Cook & Campbell, 1979). Unfortunately, our extensive literature review revealed no randomized experiments evaluating
the effectiveness of dating sites or of the matching proceduresthat these sites use.

27
How could researchers at eHarmony or some other matching site design scientifically valid research to determine
whether their company’s algorithm does what they purport it
does? The optimal randomized experiment would include atleast three control conditions to pit against the algorithm condition, in which participants are matched with potential partners according to the site’s current algorithm. The first control
condition would be a so-called wait-list control condition(sometimes called a no-intervention control condition), in
which participants who are interested in registering for thematching site are only allowed to register after the study has
concluded. The second control condition would be a placebo
control condition, in which the information about the matching algorithm provided to participants is identical to the information provided to participants in the algorithm condition(e.g., that it is based on scientific evidence and that it is likely
to introduce you to your soulmate), but participants are in fact
assigned to be matched with partners at random (within certain constraints, including sexual orientation, age and education ranges, etc.). The third control condition would be amatchability-only control condition, in which the information
about the matching algorithm provided to participants is once
again identical to the information provided to participants inthe algorithm condition, but only those aspects of the algorithm relevant to participants’ general relationship potential—and not any aspects associated with dyadic compatibility
per se—are used in the matching process. The inclusion ofthese control conditions could potentially allow the researchers to establish that any possible benefits that may emerge in
the algorithm condition are due to the algorithm itself rather
than to certain artifacts. In particular, the wait-list control
condition could help them rule out self-selection processes,
the placebo control condition could help them rule out expectancy effects, and the matchability-only control condition
could help them rule out the possibility that the algorithm iseffective for reasons that are entirely unrelated to dyadic compatibility (i.e., that any possible benefits of the algorithm aredue exclusively to the exclusion of certain people from thedating pool).
If the researchers can demonstrate—in an experimentemploying both random assignment to condition and longitudinal procedures and in a report that fully discloses the researchmethods and statistics—that participants assigned to the algorithm condition experienced better romantic outcomes thanparticipants in all three control groups, that will likely constitute convincing evidence for the validity of the site’s algorithm. Of course, demonstrating that a given compatibility
algorithm is more effective than an alternative compatibility
algorithm (e.g., that eHarmony’s algorithm is more effective
than, say, PerfectMatch’s or Chemistry’s) would require one
or more additional control conditions, which could either be
added to the four-condition experiment described above or
included in a separate experiment pitting various matchingalgorithms against one another.

28

Finkel et al.

Claims of superiority—conclusion. In sum, online datingsites frequently claim that people will achieve better romantic
outcomes when seeking partners through their site than
through conventional offline dating (or through other datingsites). Such claims have been trumpeted by news organizations that frequently celebrate dating sites’ services without
providing sufficient critical analysis, and regulatory agencies
have, it seems, adopted a laissez-faire attitude toward theseclaims. Our investigation suggests, however, that dating sites
have failed to provide any compelling evidence for theseclaims. As such, the claims simply cannot be accepted as valid.
The severe limitations of dating sites’ scientific reports areespecially disconcerting when they are compared to the rigorous scientific standards for reporting research in other areasthat have immediate relevance to the public welfare. For
example, according to the author guidelines of the Journal of
the American Medical Association (JAMA), all articles must
include at least one named author who is independent of any
commercial funder or sponsor and who indicates that she or he“had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data
analysis” (JAMA, 2011, para. 22). Furthermore,
for industry-sponsored studies, an analysis of the data(based on the entire raw data set and evaluation of thestudy protocol and a prespecified plan for data analysis)must be conducted by an independent statistician at an
academic institution rather than by statisticians
employed by the sponsor or by a commercial contract
research organization . . . The results of this independent
statistical analysis should be the results reported in the
manuscript.
Given the potentially serious consequences of intervening
in people’s romantic lives, one could argue that the standards
required of online dating sites’ scientific investigations shouldbe especially high. It is unfortunate that they have been, todate, especially low.
With this skepticism of dating sites’ claims as the backdrop, we now review a broad range of scientific literatures to
examine whether the presence or form of access, communication, and matching offered by online dating sites is likely toyield superior romantic outcomes. In the absence of studies
directly investigating the superiority question, we draw on the
rapidly expanding empirical literatures on the implicationsof access, communication, and matching for subsequent relationship outcomes to make informed evaluations of these
services.

Service #1—access: Does the access offered via
online dating yield superior outcomes?
The first service that dating sites offer is access, which refers
to users’ exposure to and opportunity to evaluate potential
romantic partners whom they are otherwise unlikely to
encounter (Step 4 in Fig. 1). As noted previously, the accessthat users acquire through dating sites does not necessarily
yield access to a relationship partner (Step 9); indeed, it does
not necessarily yield any face-to-face encounters (Step 8) oreven any mutual mediated communication (Step 7). Rather, it
simply alerts users to the existence of specific potential partners—and, reciprocally, allows such potential partners to
become aware of users’ existence. Several factors influence
the amount of users’ access on a given dating site, including
the number of people who have posted profiles on that site intheir geographical area, users’ search criteria (e.g., age range,
geographic proximity, income), and whether the dating siteallows them to view only the handful of potential partners
whom the dating site’s mathematical algorithm determines tobe matches for them.
All else being equal, the access to potential partners offeredby online dating can be a major boon to singles looking forpartners. Accessing partners through online dating helps toaddress many problems that can plague conventional
approaches to dating. Perhaps the most significant problem itsolves is the actual or perceived lack of access to potential partners offline. It also addresses the interrelated problems of having to (a) confront uncertainty about whether a given potential
partner is in fact romantically available and (b) muster thecourage to approach strangers face-to-face. To be sure, theaccess that online dating sites provide has its own limitations,
not least of which is the consumption of time and otherresources that singles might otherwise spend seeking partners
through more traditional means. However, access throughonline dating is much more convenient than access throughconventional offline dating; users have access while drinking
their morning cup of coffee with their hair rollers in, whenlying in bed for 10 minutes before turning off the light to sleep,and during meetings at work. The degree to which the accessprovided by online dating sites ultimately yields positive romantic outcomes, especially relative to conventional offline dating,depends in part on how effective the access is at facilitating
face-to-face contact with potentially compatible partners.
In this section, we evaluate whether the ways dating sites
offer access to potential partners are likely to yield superiorromantic outcomes. In particular, we examine the likely
romantic consequences of, and the assumptions underlying,
three ways access through online dating differs from accessthrough conventional offline dating. First, online dating sites
typically offer initial access to potential partners through profiles rather than through face-to-face encounters (profiles vs.
face-to-face). Second, they typically present users with nearsimultaneous access to profiles of more than one potential
partner rather than with one specific potential partner (joint vs.
separate evaluation context). Third, and related to the second
point, they frequently present users with large choice sets ofpotential partners, often hundreds or thousands of them (large
vs. small choice sets). Although these procedures characterizesome dating sites more accurately than others, they remain
more the rule than the exception.
It is easy to understand why the online dating industry
altered access in these three ways, right from Match’s launch

Online Dating
in 1995. After all, it is efficient for users to browse many profiles side-by-side as a first step toward the goal of identifying
potential compatible partners. Users can browse many profiles
quickly, allowing them to whittle down the options to a manageable number of potential partners who meet or exceed their
desirability threshold. However, critical implicit assumptions
lurk in this approach, and several lines of scientific work suggest that these assumptions, although intuitively appealing,might be flawed. The first assumption is that people tend tohave good insight into what partner qualities presented in a
profile will actually appeal to them when they meet the person
represented in that profile. The second assumption is that sideby-side comparisons among multiple potential partners provide an effective means of evaluating one’s potential attraction
to (or relationship satisfaction with) a given partner once a
relationship starts to develop. The third assumption is thatpeople make better decisions when they can choose from alarge rather than a small choice set. Next, we review a broad
range of scientific findings to evaluate the validity of thesethree assumptions.
Can users glean accurate compatibility informationfrom profiles? Self-selection and algorithm-selection sites
make different assumptions regarding who is best suited to
identify compatible matches for users—the users themselves
or a particular matching algorithm. We postpone discussionof the validity of matching algorithms until the “Service #3—
Matching” section below; for now, we examine the degree
to which users are well served by trusting their intuitions
regarding their own idiosyncratic preferences for a romantic
partner. People typically believe they know what they desire ina potential partner—what partner characteristics, as assessed
through a profile, are especially compatible with them. Indeed,
after conducting in-depth interviews with online daters, Long
(2010, p. 209) concluded that all of them “knew what they
were looking for . . . and searched through several people andprofiles to find it.” Is users’ confidence in their introspective
accuracy justified?
There are actually two ways of posing this question, andthey yield different answers. First, can people glean sufficient
information from profiles to assess which potential partners
are generally more appealing than others? Second, can people
glean sufficient information from profiles to assess which
partners are uniquely compatible with them? The answer to thefirst question (assessing which partners are appealing in general) appears to be yes. When browsing profiles, users canglean accurate information not only regarding obvious traits
such as physical attractiveness but also regarding subtler traits,
such as warmth and competence. Indeed, research on “thinslices” of social behavior demonstrates that individuals canaccurately assess a broad range of trait-relevant information
about other people based on brief exposure to their photographs (Ambady, Bernieri, & Richeson, 2000; Wills &Todorov, 2006). For example, participants’ independent ratings of the power and leadership ability exhibited in facial

29
photographs of chief executive officers from Fortune 1,000
companies exhibited moderately strong correlations with thecompanies’ profits (Rule & Ambady, 2008), which is consistent with the conclusion that powerful chief executive officers
with strong leadership ability both exhibit these qualities infacial photographs and use these skills to generate profits.
Such effects, which emerge even though the participants do
not recognize the people depicted in the photographs, suggestthat people can glean accurate trait information from photographs and, by extension, from online dating profiles.
However, the answer to the second question (assessing
which partners are uniquely compatible with a given user)
appears to be no. Indeed, the ability to glean general desirability information might not be especially beneficial because theease of contacting extremely desirable people in online dating
may undermine the pairing process. Users contact some potential partners at massively higher rates than others (Hitsch et al.,2010a), a tendency that is typically mitigated in more conventional approaches to dating. In the words of Christian Rudder,
one of the founders of OkCupid (quoted in Paumgarten, 2011,
p. 43),
You’ve got to make sure certain people don’t get all theattention. In a bar, it’s self-correcting. You see ten guys
standing around one woman, maybe you don’t walkover and try to introduce yourself. Online, people haveno idea how “surrounded” a person is.
Rudder observes that this lack of knowledge can create an
unfortunate dynamic in which women become overwhelmed
and become increasingly unlikely to reply to the messages
men send to them. In short, although people appear to be able
to judge which potential partners are more appealing than others based on the sorts of information that typically appear in
profiles, the ease with which any online dater can contact themost appealing potential partners implies that such judgments,
by themselves, cannot provide an efficient pairing process.
Not only are users at risk for contacting potential partners who
are out of their league, but the most desirable partners, especially the most desirable women, are likely to find the processof sifting through so many first-contact e-mails aversive, perhaps causing them to disengage from the process altogether.
Even leaving aside this deluge problem, users’ ability todetect from a profile which partners are consensually appealing falls short of the goal of enabling users to identify potential
partners who are uniquely compatible with them. This observation links to the second way of posing the question ofwhether users’ confidence in their introspective accuracy isjustified: Does the ability to judge which partners are more
appealing than others suggest that people can glean sufficient
information from profiles to assess which partners are uniquely
compatible with them? For example, if John prioritizes a particular trait (e.g., competence) more than James does whenbrowsing profiles, will a potential partner who possesses that
trait inspire John’s romantic interest more than James’s once

30
the men have met the potential partner in person? A recent
program of research, which examined the extent to which people tend to be attracted to potential partners who match versus
mismatch their stated ideal partner preferences, suggests thatthe answer is no (Eastwick & Finkel, 2008a). People often
lack insight into why they like things (Nisbett & Wilson,
1977), perhaps including which characteristics available on apotential partner’s profile will inspire their attraction to him or
her following a face-to-face encounter (Eastwick & Finkel,
2008a; Sprecher, 1989).
In support of this lack-of-insight idea, people’s idiosyncratic self-reported preferences for certain characteristics inhypothetical romantic partners appear to be irrelevant to theirromantic outcomes with specific potential partners they haveactually met in person. In one study, for example, undergraduate participants reported the degree to which they desired
potential romantic partners who possessed three characteristics that are frequently assessable in profiles: physical attractiveness, earning prospects, and personability (Eastwick &Finkel, 2008a). Approximately 10 days later, they attended a
speed-dating event where they met 12 potential partners, evaluating every one of them on these same three traits. Not surprisingly, participants experienced greater attraction to theextent that potential partners possessed these desirable characteristics—everybody prefers attractive, friendly partners withgood earning prospects to ugly, unfriendly partners perpetually freeloading off of their parents. However, these associations were irrelevant to individual differences in statedpreferences for these characteristics (Eastwick & Finkel,
2008a; also see Finkel & Eastwick, 2008). For example, physical attractiveness inspired romantic interest, but physicalattractiveness did not have a stronger association with romantic interest for those participants who had reported that theystrongly (vs. weakly) desired this trait in an ideal partner.
This disconnect between a priori stated preferences and invivo revealed preferences (those preferences that people
exhibit after actually meeting potential partners) has emerged
not only in undergraduate samples but also in speed-dating
samples ranging from graduate students at an elite university
in the United States (Iyengar, Simonson, Fisman, & Mogilner,2005) to clients of a professional speed-dating company inGermany (Todd, Penke, Fasolo, & Lenton, 2007). It has also
emerged in studies using experimental and longitudinal
designs. For example, of particular relevance to the present
analysis, when research participants evaluated a potential
romantic partner’s written profile (no photograph included),
they expressed more romantic interest in a partner whose traits
were rigged to match (vs. mismatch) their idiosyncratic preferences (Eastwick, Finkel, & Eagly, 2011). After a brief live
interaction with the potential partner, however, the extent towhich the partner matched their ideals no longer predictedromantic interest. In other words, participants exhibited a disconnect between (a) their idiographic self-reports of the traits
that they desired in a romantic partner and (b) the traits that

Finkel et al.
actually predicted their romantic attraction to and relationshipwell-being with real-life potential partners. This effect is not
limited to initial attraction contexts: A longitudinal study of
middle-aged adults demonstrated that people with particularly
strong preferences for a specific partner characteristic did notexperience greater desire for their current partner to the degree
that he or she possessed that characteristic (Eastwick, Finkel,& Eagly, 2011).
Taken together, these studies suggest two conclusions.
First, people’s idiosyncratic stated preferences predict who
appeals to them in a profile but not necessarily who appeals tothem in person. Second, although people can accurately discern from profiles which potential partners are more appealing
than others, they may not be especially good at discerningwhich characteristics will uniquely inspire their attraction andrelationship well-being after meeting the potential partner inperson, and this disconnect may cause certain potential partners to receive huge numbers of first-contact e-mails. As aresult, such consensually desired partners are likely to become
decreasingly likely to reply to these emails and perhaps
increasingly likely to opt out of the process. As a whole, theonline dating system would function more effectively if there
were some mechanism by which users could discern which
potential partners are more compatible with them than withother users. Browsing profiles does not appear to be such amechanism.
Are side-by-side comparisons a good way to evaluate
potential romantic partners? Even if browsing profiles werean effective means of evaluating one’s compatibility with apotential partner, the access function is frequently implemented
by dating sites in a manner that further undermines theusefulness of that access. For example, one major problem
may emerge from the disconnect between the characteristics
of potential partners that users are likely to prioritize whenbrowsing profiles and those that matter when pursuing a
relationship with a specific potential partner. Browsing profiles
places people into a joint evaluation mode in which they
compare multiple potential partners nearly simultaneously,
whereas pursuing a relationship with a particular partner places
people into a separate evaluation mode in which they evaluate
one specific partner in isolation.8
Frequently, people make choices in a joint evaluation modebut experience the consequences of those choices in a separate
evaluation mode. For example, “when buying a sofa, we usually compare multiple options, whereas when using the sofa
we have eventually bought, we face that sofa alone” (Hsee &Zhang, 2010, p. 349). Similarly, when browsing profiles, auser compares multiple options with one another (even if it isjust a handful of options selected by an algorithm), whereas
when he or she initiates and pursues a relationship with apotential partner met through a dating site, that relationship isfrequently evaluated on its own. Rather than comparing multiple potential partners, the user is evaluating whether aOnline Dating
specific potential partner is or is not a good fit for him or her.
This distinction might seem subtle, but an impressive programof research suggests that it is consequential.
Do users prioritize the wrong characteristics when browsing
profiles? Many studies in nonromantic domains havedemonstrated that people frequently prioritize different
qualities when in a joint evaluation mode than they do whenin a separate evaluation mode (Hsee, Loewenstein, Blount,& Bazerman, 1999). Of particular relevance to online dating,people in a joint evaluation mode (e.g., when browsing
profiles) frequently overemphasize certain attributes thatare relatively unimportant in a separate evaluation mode(e.g., when pursuing a relationship with a potential partner
initially encountered when browsing profiles). For example,
before learning to which of 12 dormitories they would be
randomly assigned, incoming college freshmen predicted thatthe physical features of the dormitories (e.g., location, roomsize) would strongly influence their future happiness once
they lived in a given dormitory; however, after they had been
living in their assigned dormitory, these physical features did
not predict their happiness (Dunn, Wilson, & Gilbert, 2003).
One explanation for this disconnect is that the freshmen (a)were in a joint evaluation mode when making the predictions
and in a separate evaluation mode when living in the assigned
dorm and (b) were more sensitive to physical variations,which were ultimately unimportant, in a joint evaluation mode
than in a separate evaluation mode because such variations
were easy to evaluate (Hsee & Zhang, 2010). By analogy,
the joint evaluation model triggered by browsing profiles ofpotential romantic partners is likely to cause users to focus on
certain qualities they think are important in a potential partner,
perhaps to the neglect of qualities that actually are important.
Such processes might be especially problematic in online
dating because the sorts of easy-to-evaluate, searchable characteristics available through profiles tend to be largely irrelevant to the sorts of hard-to-evaluate, experiential characteristics
that promote positive outcomes in an emerging or an established relationship (Frost et al., 2008). Experiential attributes
are those that are best evaluated face-to-face (e.g., rapport,
sense of humor), whereas searchable attributes can be evaluated through a simple informational presentation, such as an
online dating profile (e.g., income, physical attractiveness). It
is difficult for an online dater to know whether he or she will
like a potential partner based on knowledge of the partner’s
searchable traits and interests, just as it is difficult for someone
to know whether or not he or she will like a meal based on
knowledge of the ingredients and nutritional content (Frostet al., 2008; Kaplan, 2008). When browsing profiles, a user
conducts a side-by-side comparison, selecting or dismissing
potential partners based upon searchable characteristics thatfacilitate judgment in a joint evaluation mode but that frequently turn out to be unimportant once the user has started a
relationship with a specific potential partner and, consequently, is in a separate evaluation mode. Indeed, preference
reversals from joint to separate evaluation modes tend to31“occur when a hard-to-evaluate attribute is more important
than an easy-to-evaluate attribute” (Gonzalez-Vallejo &Moran, 2001, p. 220), as in online dating, where importantqualities like sense of humor and rapport are difficult to evaluate from a profile.
Does the side-by-side comparison process elicit anassessment mindset that undermines romantic outcomes? A
second consequence of having users engage in side-by-side
comparisons of potential partners is that the joint evaluation
mode that is triggered by this process is likely to strengthen
assessment mindsets and undermine locomotion mindsets(Kruglanski et al., 2000). An assessment mindset stresses
critical evaluation of entities, states, or goals in comparison
to available alternatives, whereas a locomotion mindset
emphasizes committing psychological resources toward theeffective and pleasurable attainment of desired goals (fora related distinction between deliberative and implemental
mindsets, see Gollwitzer & Bayer, 1999). Of course, all dating,especially when first getting to know a potential partner,
involves some degree of assessment. We suggest, however,that online dating—in which users frequently make mating
decisions after browsing a profile for a matter of seconds
and in which the next potential partner is a mere mouse-clickaway—may place an unusually high priority on this mindset,
especially relative to the priority placed on a locomotion
mindset.
What are the likely consequences of adopting a strong
assessment mindset and a weak locomotion mindset? On one
hand, adopting an assessment mindset likely facilitates certain
aspects of the online dating process. For example, an assessment (or a deliberative) mindset is associated with (a) pursuing the optimal choice among an array of options (Kruglanski
et al., 2000), (b) less biased consideration of the pros and consof alternatives (Taylor & Gollwitzer, 1995), and (c) moreaccurate forecasts about the future of a romantic relationship(Gagné & Lydon, 2001). On the other hand, an assessment
mindset may not promote satisfying social interaction. First
encounters are often awkward, and an assessment mindset
may channel the assessor’s attention in ways that inhibit fluid,
spontaneous interaction. Even in established relationships,
people with strong assessment or weak locomotion mindsets
tend to be especially critical and unsupportive of their partners(Kumashiro, Rusbult, Finkenauer, & Stocker, 2007) and pessimistic about both the future of their relationships (Gagné &Lydon, 2001) and their ability to complete goal-relevant tasks(Armor & Taylor, 2003; Kruglanski et al., 2000)—all factorsthat predict unsuccessful, dissatisfying relationships over thelong term (Kumashiro et al., 2007).
In addition to undermining relationship dynamics in emerging and existing relationships, an assessment mindset cancause problems during the profile-browsing stage, despite
potentially making the process more efficient. Specifically, as
discussed previously, the browsing process can cause users to
objectify potential partners, commoditizing them as options
available in a marketplace of profiles (Heino et al., 2010;

32
Lawson & Leck, 2006). Perhaps more than in conventional
offline dating, an online dater may immediately assess thelikely rewards and costs associated with forming a relationship with a certain partner, relative to alternatives, and use
these assessments to decide whether to pursue further contact(Sunnafrank, 1988). Ahuvia, Adelman, and Izberk-Bilgin(2009, p. 239) referred to this process as “a ƒMcDonaldizing’of romance that is systematizing, rationalizing, and rendering
into a calculative mate quest” a process that perhaps functions
better when people adopt a less deliberate approach. Indeed,close relationships are especially satisfying to the degree thatthey involve (a) relatively high levels of social exchanges thatare low in concreteness and high in particularism, such as love
and affection; and (b) relatively low levels of social exchanges
that are high in concreteness and low in particularism, suchas tangible goods and money (Foa, 1993). Similarly, close
relationships are especially satisfying to the degree thatthey are characterized by a strong communal orientation, in
which partners provide benefits according to each other’s
needs, and a weak exchange orientation, in which partners
provide benefits according to reciprocity norms (Clark, 1984;
Clark, Lemay, Graham, Pataki, & Finkel, 2010). It seems
reasonable to assume, then, that the sort of marketplaceexchange orientation triggered by the profile-browsing process might interfere with the development of satisfying
romantic relationships.
Does selecting potential partners from large choice sets
improve romantic outcomes? Online dating sites, especiallyself-selection sites, frequently offer users more than just a
handful of profiles for side-by-side comparison. Indeed, theyoften present users with nearly simultaneous access to (theprofiles of) many potential partners, sometimes thousandsof them, who vary in complex ways across a wide range ofcharacteristics. As noted previously, this situation differs
markedly from traditional dating, where singles rarely encountera large array of potential partners at once, especially in a settingin which they can almost effortlessly initiate correspondencewith many of them. Even at a party, it typically is not obvious
which partygoers are open to starting a romantic relationship,and even if the party is explicitly designed for singles, the public
setting and the presence of so many competitors for potential
partners’ affections means that people must recruit substantial
courage to approach potential partners.
Do large choice sets yield choice overload, lazier decisionmaking strategies, and poorer decisions? Beyond triggering a
joint evaluation mode and an assessment mindset, both ofwhich can arise even with relatively small choice sets, how
might providing large choice sets influence the manner inwhich users browse profiles and prioritize some potential
partners over others? Certainly, people value choice (Leotti,
Iyengar, & Ochsner, 2010), and the experience of choice
causes elevated activation in brain regions associated withmotivation and reward, such as the ventral striatum (Leotti

Finkel et al.
& Delgado, 2011). Furthermore, the perception that one has
options (and the autonomy or responsibility to select amongthem) predicts psychological and physical health (Deci &Ryan, 1991; Langer & Rodin, 1976; Ryan & Deci, 2001).
Indeed, people frequently strive to keep their options open
even when doing so yields worse outcomes than would
jettisoning clearly inferior options (Botti & Hsee, 2010; Botti& Iyengar, 2004; Botti, Orfali, & Iyengar, 2009; Bown, Read,& Summers, 2003; Chernev, 2006; Shin & Ariely, 2004; forreview monographs, see Iyengar, 2010; B. Schwartz, 2004).
Despite the strength of people’s motivation to keep theiroptions open, however, many studies have demonstrated that alarge degree of choice can overwhelm people, undermining
their ability to make good decisions and sometimes producing
a state of choice overload, in which people simply avoid making any decision rather than exerting the mental effort required
to compare and contrast so many options (Iyengar, 2010;
Keller & Staelin, 1987; Malhotra, 1982; Redelmeier & Shafir,
1995; B. Schwartz, 2004; Simonson & Tversky, 1992; for a
recent debate regarding choice overload and paralysis, see
Scheibehenne, Greifeneder, & Todd’s, 2010, meta-analytic
review and Chernev, Bockenholt, & Goodman’s, 2010, commentary). In a famous illustration of this principle, supermarket shoppers encountered a tasting booth that displayed either
6 or 24 flavors of jam (Iyengar & Lepper, 2000). Although
shoppers were 50% more likely to stop at the tasting boothwith the larger array of jams than at the booth with the smaller
array, they were 10 times more likely to purchase one of thejams from the smaller array than to purchase a jam from thelarger array (30% vs. 3%).
Recently, scholars have extended this line of research
into the romantic domain, addressing three research questions
about the influence of large versus small choice sets of potential partners. First, does choice set size influence people’s subjective experience of the choice process? Second, do relatively
large choice sets cause people to employ less cognitively taxing comparison strategies and, consequently, alter their mating
decisions? Third, do relatively large choice sets cause people
to make mating decisions that are less closely aligned with
their idealized mating preferences, perhaps undermining theirlikelihood of experiencing positive romantic outcomes withpotential partners they have met through the dating site?
Regarding the first question—whether choice set size influences participants’ subjective experience of the choice process—two experiments suggest that the answer is no (Lenton, Fasolo,& Todd, 2008; Lenton & Stewart, 2008). Participants in theseexperiments were randomly assigned to view choice sets ranging from 4 to 64 online dating profiles, and they experienced
comparable levels of satisfaction and enjoyment regarding theprocess across the choice set size conditions. It is plausiblethat online daters do indeed become less satisfied with thesearch process as the number of browsed profiles gets into thehundreds or thousands, but additional research is required toaddress this issue.

Online Dating
Regarding the second question—whether large choice sets
cause participants to employ lazier comparison strategies and,
consequently, alter their mating decisions—several studies
suggest that participants become cognitively overwhelmed as
choice sets of potential partners grow larger. For example, participants were more prone to memory confabulations, misremembering characteristics as present in a particular profile
when they were in fact absent, when browsing 20 rather than 4
online dating profiles (Lenton et al., 2008). In addition, as
choice set size increased from 4 to 24 to 64 profiles, users
increasingly switched from time-consuming choice strategies
that examine and combine multiple cues (e.g., weighted averaging; Payne, Bettman, & Johnson, 1993) to more frugal
choice strategies that examine few cues and do not maketrade-offs among conflicting ones (e.g., “elimination by
aspects”; Tversky, 1972) (Lenton & Stewart, 2008).
Speed-dating studies tell a similar story. For example, relative
to speed-daters at smaller events (15–23 speed-dates), the “yessing” decisions of speed-daters at larger events (24–31 speeddates) were less strongly predicted by characteristics that are
relatively hard to elicit and evaluate, such as occupational status
and educational attainment, and more strongly predicted by characteristics that are relatively easy to evaluate, such as height andweight (Lenton & Francesconi, 2010). Consistent with this evidence, participants exhibit stronger consensus regarding which
potential partners are most appealing at larger relative to smaller
events (Lenton, Fasolo, & Todd, 2009). This effect could readily
emerge if speed-daters increasingly prioritized characteristics that
required minimal cognitive resources to assess (e.g., physicalattractiveness) and if speed-daters exhibited some level of consensus regarding which partners possessed such characteristics.
Regarding the third question—whether large choice sets
cause participants to make mating decisions that are lessclosely aligned with their idealized mating decisions, perhaps
undermining their chances of establishing successful relationships with potential partners on the dating site—recent studies
suggest that the answer may be yes. For example, relative toparticipants browsing smaller choice sets of online dating profiles, participants browsing larger choice sets (whether comparing sets of 30, 60, and 90 or sets of 40 and 80) choose
partners who diverge more from participants’ own stated preferences (Chiou & Yang, 2010; Wu & Chiou, 2009; Yang &Chiou, 2010). In addition, larger choice sets cause participants
to allocate their time poorly, spending too much time studying
profiles of potential partners who are poorly matched to theirstated preferences and too little time studying profiles ofpotential partners who are well-matched (Wu & Chiou, 2009).
These studies suggest that users make mating decisions thatare poorly aligned with their stated preferences when theybrowse large choice sets of potential partners, which is consistent with the finding that they employ relatively careless cognitive strategies under such circumstances. However, critiquing
users’ mating decisions based solely upon the convergence ofsuch decisions with users’ stated preferences provides anincomplete analysis of the quality of those decisions; after all,33
a priori stated preferences may not reliably align with in-vivo
preferences following face-to-face interaction (e.g., Eastwick& Finkel, 2008a; Eastwick, Finkel, & Eagly, 2011). As such, it
is important to bring additional evidence to bear, from bothwithin and beyond the romantic domain, to address whether
large choice sets are likely to cause users to experience choiceoverload or diminished satisfaction with the choices they havemade.
Several such studies are relevant. For example, a recent
speed-dating study provided two suggestive findings (Lenton& Francesconi, 2011). First, as the variability of potential partners increased (across attributes like age, height, occupation,and education), speed-daters became increasingly likely to say“no” to 100% of their partners, echoing the choice overload
effect in which supermarket shoppers failed to make a purchase when encountering a large array of jams. Second, as thevariability of potential partners increased, speed-daters
became decreasingly likely to say “yes” to those potential
partners who were consensually viewed as appealing, echoing
the careless decision effects in which online daters’ mating
decisions converged poorly with their stated preferences when
encountering a large array of profiles. Although this speeddating study focused on the variability of the choice set rather
than its absolute size, it is likely that the mechanism underlying the effects in both contexts (large and variable choice sets)
involves the complexity of the information the decision maker
confronts.
Another speed-dating study addressed choice set size more
directly (Fisman, Iyengar, Kamenica, & Simonson, 2006).
Graduate students attended speed-dating events that varied insize from 18 participants (9 of each sex) to 42 participants (21of each sex). As the size of the choice set increased—as speeddaters met a larger number of opposite-sex partners—they said
“yes” to a smaller proportion of the potential partners, although
the effect was limited to female speed-daters in this study.
Specifically, women who attended speed-dating events at
which they met a relatively small number of men (9–14) wereover 40% more likely to agree to a follow-up meeting with any
given potential partner than were women who attended events
at which they met a larger number of men (15–21), an effectthat is consistent with the choice overload effect.
No studies in the romantic domain have examined satisfaction with choices after selecting from larger versus small
choice sets of potential partners, but research outside theromantic domain suggests that people are likely to be less satisfied when choosing from a larger choice set. For example,
people who selected a chocolate from an array of 6 chocolates
thought the chocolate tasted significantly better than did participants who selected their chocolate from an array of 30, andthey chose to be compensated for their research participationin chocolate rather than in cash four times more frequently(48% vs. 12%; Iyengar & Lepper, 2000). Analogously, online
daters choosing from relatively large (vs. small) choice sets
may be less satisfied with the potential partners they selectfrom those choice sets.

34
Of course, in the studies reviewed in this section, the large
choice set condition was always operationalized with fewer
than 100 choices, which is a far cry from the hundreds or thousands of potential partners that many online dating sites offer tousers. We speculate that the effects reviewed in the preceding
paragraphs would be, if anything, even stronger at such sites.
Do large choice sets make people less willing to commit to one
person? A second possible consequence of offering users accessto large rather than small choice sets is related to the first:
Having immediate access to (the profiles of) so many potential
partners might undermine users’ willingness to commit to any
one person. Indeed, a vast corpus of research demonstrates
that the perception of and attention to potentially appealing
alternatives to a current romantic partner is a strong predictor ofboth low commitment to that partner and eventual relationshipbreakup (Drigotas & Rusbult, 1992; Miller, 1997; Rusbult,
1983; for meta-analytic reviews, see Le & Agnew, 2003; Le,
Dove, Agnew, Korn, & Mutso, 2010). Furthermore, people
tend to exhibit stronger positive illusions regarding specificpotential partners when they believe that access to alternative
potential partners is scarce rather than plentiful (e.g., Gladue& Delaney, 1990; Pennebaker et al., 1979). As observed byCoren (2006), this tendency is likely to be accentuated by theconstant availability of romantic alternatives through online
dating sites: “And now, with globalisation, we actually havemillions of partners to choose from. We end up frozen overthe procedure, constantly refining and refining, certain that aneven more congruent soul mate is waiting in the next personal
column or on the next website” (para 13). These empiricalfindings and observations converge to suggest that the constantaccess to large numbers of potential partners might undermine
users’ willingness to commit, or to remain committed, to aparticular partner.
Access—conclusion. Online dating sites offer a broad rangeof access that users would otherwise lack. In addition, they
offer access in extremely convenient and flexible formats.
These are two major benefits of online dating compared toconventional offline dating.
When shifting attention from the presence of the accessprovided by online dating to the specific implementations of
this access, the degree to which dating sites benefit users
becomes less clear. For example, providing access to potential
partners via profiles artificially transforms three-dimensional
people into two-dimensional displays of information, and it
appears that something crucial is lost in the transformation.
Although users are likely to detect which potential partners
possess certain appealing qualities by browsing profiles, they
may be incapable of determining which potential partners are
uniquely suited to them. In combination, these factors can leadto a situation in which a small number of particularly appealing potential partners become deluged with first-contact
requests, which can cause them to disengage from theprocess.

Finkel et al.
In addition, evaluating multiple potential partners nearly
simultaneously, rather than evaluating one at a time, is likely
to alter users’ decision processes in a manner that could potentially undermine their romantic outcomes with a particularpotential partner. People tend to prioritize different qualities
when conducting joint evaluation (as in browsing) than when
conducting separate evaluation (as in determining whether a
specific potential partner is appealing). As such, they mightprioritize attributes during the browsing process that are irrelevant to, or perhaps even contradictory to, the attributes thatwill actually make them happy once a face-to-face relationship starts to form.
Finally, online dating sites frequently offer users a whole
lot more than simply “more than one” potential partner; theyfrequently offer hundreds or thousands of them. Indeed, andperhaps ironically, one of the most prominently advertised features self-selection sites use to lure singles is immense numbers of available partners. Online daters are likely to find theprocess of browsing through so many profiles cognitively
laborious, which may decrease their level of interest in any
specific potential partner during the browsing process—and it
might ultimately undermine their levels of happiness with andcommitment to a potential partner once a relationship moves
offline.
In short, online dating sites offer extensive and convenient
access in ways that were virtually nonexistent in conventional
offline dating. These considerable advantages are mitigatedsomewhat by the widespread emphasis on introducing users topotential partners through profiles, which fail to capture theessence of a person. Fortunately, dating sites provide morethan access to new potential partners; they also provide a convenient means of communicating with those partners via
CMC—as a means toward the goals of learning more about
potential partners and perhaps ultimately setting up a face-toface meeting.

Service #2—communication: Does thecommunication offered via online dating yieldsuperior outcomes?
Outside of online dating, it is possible but rare that people
would have a chance to communicate with each other before
meeting in person; for example, two people who have agreed
to a blind date might need to correspond to determine an initial
meeting location. However, users of dating sites are certain tocorrespond via CMC before meeting in person. Is CMC simply a logistical necessity in the early phases of an online dating
relationship, or might engaging in CMC before meeting faceto-face have implications for the initiation and development ofrelationships as the users move offline? Research that spansthe fields of communication, social psychology, and relationship science suggests that the widespread use of CMC in
online dating may have significant, if not always intended,
implications for the character and success of offline relationships that may follow.

Online DatingThis section on the communication service of online dating
addresses two questions. First, can CMC foster intimacy andliking between strangers? Second, can CMC substitute forface-to-face interaction? Throughout this section, we compare
CMC to face-to-face interaction within the context of the getacquainted process, and we review research from both romantic and nonromantic contexts. We place less emphasis on the
differences between alternative forms of CMC (e.g., e-mail vs.
instant messaging), as these comparisons remain “considerably
undertheorized and understudied” (Walther, 2010, p. 495).
Can CMC foster intimacy and liking between strangers?
Today, many people are comfortable with the idea that actualsocial relationships can form and persist online (Madden &Lenhart, 2006). But when CMC first became widely available
with the advent of networked computer systems in the 1970s
and the proliferation of dialup bulletin board systems in the
1980s, scholars and laypeople alike were dubious that CMC
could foster a meaningful social connection, let alone that
actual relationships could emerge without some face-toface contact. Early research tackled this topic by examining
the ability of workgroups to achieve consensus and to makedecisions effectively when meeting over a computer network
versus via traditional face-to-face interaction (e.g., Kiesler,
Siegel, & McGuire, 1984; for a review, see Baym, 2006).
Confirming skepticism about the relationship-building
potential of CMC were the findings that, in computermediated compared to face-to-face settings, people (a) weremore likely to behave in a task-oriented, impersonal mannerand also (b) exhibited more disinhibited and counternormative
behaviors, such as flaming (i.e., the expression of strong
and inflammatory opinions; Hiltz, Johnson, & Turoff, 1986;
Siegel, Dubrovsky, Kiesler, & McGuire, 1986). These findings
suggested multiple possibilities: Relative to face-to-faceinteraction, CMC might not foster relationship formation
because of the reduced informational feedback and/or because
a sense of depersonalization freed people from adhering tostandard social norms (Kiesler et al., 1984).
Cues-filtered-out is an umbrella term that describes theseearly findings (Culnan & Markus, 1987). Building on earlier
telecommunications research (e.g., Daft & Lengel, 1984; Short,
Williams, & Christie, 1976), the cues-filtered-out perspective
suggested that because CMC possesses fewer nonverbal (e.g.,dress, facial expression, posture, mimicry), contextual, andauditory cues than face-to-face interaction does, it offers a
reduced experience of social presence. Furthermore, because
these reduced cues cause CMC to feel unlike “real” interaction
to the user, people are more likely to engage in impersonal, asocial forms of communication and may even exhibit more uninhibited, antisocial tendencies than they would in face-to-facesettings. In short, cues-filtered-out suggests a three-step process: (a) Reductions in the number of available nonverbal andcontextual cues will (b) diminish people’s experience of social
presence and use of intimate, social communication, thus (c)hindering people’s ability and desire to form social connections

35(see also Rice & Love, 1987). Thus, the cues-filtered-out perspective generated little confidence that CMC could foster real
relationships, much less deeply intimate romances.
New theories that emerged in the 1990s challenged thecues-filtered-out perspective, taking specific aim at the presumed link between the number of available cues and the useof social forms of communication—link (a) to (b) in the previous paragraph. Especially noteworthy was a meta-analysis of
this early research by Walther, Anderson, and Park (1994),
which found that users assigned to have a discussion using
CMC were less likely to engage in friendly, social forms ofcommunication than face-to-face discussants only when thediscussions were time-limited. When discussions had unrestricted time frames (as would a fledgling relationship), CMC
and face-to-face communication formats did not differ in therelative amount of social communication elicited. Walther andcolleagues (1994) interpreted these findings in the context ofsocial-information-processing theory (Walther, 1992; Walther,1995), which suggests that people communicate social information within the limitations of the medium. In other words,
because humans are naturally social creatures, they will use thecommunicative tools they have at their disposal to connect with
one another even in the absence of nonverbal and other sociallyrelevant cues. For example, CMC users consider the social
information inherent in the timing of responses (Walther &Tidwell, 1995): Fast (vs. slow) e-mail responses communicate
greater warmth and social support (Ledbetter, 2008). Social
information can also be conveyed through emoticons (Derks,
Bos, & von Grumbkow, 2007) as well as language style andword choice (Walther, Loh, & Granka, 2005). Even thoughmany early influential theories of relational development moreor less assumed that at least some amount of face-to-face contact was required for intimacy to emerge (e.g., Altman & Taylor, 1973), modern communications perspectives recognizethat CMC permits the exchange of a great deal of social information and that intimate relationships can indeed be forgedthrough CMC (A. Cooper & Sportolari, 1997; Lea & Spears,
1995; Merkle & Richardson, 2000).
In fact, as research paradigms have shifted away from thestudy of workgroups and toward dyadic interactions, theemerging consensus is that CMC may foster more social presence, and hence more intimacy and desire for social connection, than face-to-face settings (Antheunis, Valkenburg, &Peter, 2007; Joinson, 2001; Schouten, Valkenburg, & Peter,
2009; L. C. Tidwell & Walther, 2002; Valkenburg & Peter,
2009a; Valkenburg & Peter, 2009b). These findings are consistent with the hyperpersonal perspective (Walther, 1996), a
special case of social-information-processing theory that highlights several features specific to CMC that can increase thelikelihood that people will form positive online impressions
relative to face-to-face interactions. On the part of the message
sender, many forms of CMC provide enhanced control overthe communication process; because people can edit andrevise text-based messages, they have more opportunities topresent themselves in a strategic manner to convey a highly

36
socially desirable image (Goffman, 1959; Walther, 1996,1997). Such strategic self-presentation might entail contextualizing negative information in a positive light, selectively
revealing negative information over time, actively suppressing
negative information, or presenting an impression that reflects
one’s ideal self or true self rather than one’s actual self (Ellison
et al., 2006; McKenna, Green, & Gleason, 2002). Also, theabsence of nonverbal channels of communication in CMC
might cause people to compensate (Argyle & Dean, 1965) bymaking more intimate disclosures in CMC than face-to-faceand, consequently, accelerating intimacy (Altman & Taylor,
1973; Reis & Shaver, 1988).
Hyperpersonalization processes may also take place in themind of the message recipient: Text formats are frequently
limited in the amount of information they convey, and perceivers are likely to elaborate on or fill in the gaps using their own
mental schemas or other known information about the sender,such as shared category membership (Spears & Lea, 1992).
Although schemas, such as attachment representations (Baldwin, 1992, Bowlby, 1969), can cause people to interpret interpersonal information to be more negative than reality (Walther,1997), in the context of initial communication with a potential
romantic partner, people on average will tend to confirm theirhopes and positive expectations. For example, a sender mightmention “loving the outdoors,” and the receiver interprets thisdisclosure to mean “I love ƒroughing it’ in the wilderness” asopposed to the sender’s intention of “I love ski weekends in
Aspen.” Thus, when a liked other sends an ambiguous textbased message, the perceiver may interpret that ambiguity in a
desired light, and ultimately this may lead the perceiver tohave an exaggeratedly favorable view of the sender. In fact,
manipulations of the extent of a partner’s self-disclosure havea larger effect on a perceiver’s feelings of intimacy in CMC
than they do in face-to-face interactions (Jiang, Bazarova, &Hancock, 2011). Furthermore, mediational analyses have suggested that this effect is due to the perceiver’s optimistic overinterpretation of the limited CMC cues: Perceivers in CMC(relative to face-to-face) contexts are more likely to believethat the self-disclosures are due to unique qualities of the
dyadic relationship (Jiang et al., 2011).
In summary, CMC encourages message senders to engage
in strategic self-presentation and to make intimate disclosures,and it also encourages message recipients to form positive
impressions of the senders. Taken together, these features cause
many dyads to experience greater attraction and intimacy after
interacting via computer compared to dyads meeting and interacting face-to-face (Jiang et al., 2011; McKenna et al., 2002; L.
C. Tidwell & Walther, 2002; Walther, 1996; but see Okdie,
Guadagno, Bernieri, Geers, & Mclarney-Vesotski, 2011).
However, even if online dating CMC services permit users
to engage in more self-disclosure and to form more positive
impressions than would occur in face-to-face communication
among their offline counterparts, ultimately, the goal of mostonline daters is to meet in person to assess the potential forforming an offline relationship. Although the communication

Finkel et al.
process at some sites, particularly eHarmony, can prolong theinitial CMC period, most online daters try to move the relationship offline quickly—65% of participants in one sample
typically did so within a week of initial CMC contact (Whitty& Carr, 2006). Therefore, the research on differences between
CMC and face-to-face impression formation that would be
most relevant to online dating involves procedures that examine participants’ impressions when they first meet through
CMC and then later transition to face-to-face interaction(sometimes called a “mixed-mode relationship”; Walther &Parks, 2002). Thus, the (small) literature on modality switching is especially relevant to the communication service offeredby online dating sites. This literature addresses how people’s
impressions change when an initial CMC interaction is followed by a face-to-face interaction.
Conceivably, the modality switch from CMC to face-toface could be associated with increases in impression positivity on average (an “enhancing” hypothesis). Some scholars
have suggested that even a brief CMC interaction (e.g., 40
minutes) fosters the ability to express one’s true self better
than a similarly brief face-to-face interaction (Bargh, McKenna, & Fitzsimons, 2002), and CMC may be especially likely
to have this true-self-revealing effect for socially anxious andlonely individuals (McKenna et al., 2002). It is therefore
plausible that online dating communication services could
build a substantive foundation for a relationship: People’s
CMC-revealed inner beauty could outshine social deficits or
unappealing physical qualities, thus permitting subsequentface-to-face interaction to promote relationship growth (A.
Cooper & Sportolari, 1997). Alternatively, a user could form
an exaggeratedly positive or overly specific impression of apotential partner through CMC, perhaps because the potential
partner engages in deception or because the user overinterprets CMC-limited cues in a favorable or overly definitive
light. In this case, an initial face-to-face interaction may provide discouraging disconfirmation of an idealized or overly
particular image (D. Jacobson, 1999; see also Aronson &Linder, 1965; Thibaut & Kelley, 1959). According to this perspective, people’s impressions might become more negativewith the switch from CMC to face-to-face interactions (a“devaluing” hypothesis).
Recent research has supported the enhancing hypothesis
and partially supported the devaluing hypotheses, with one
key moderating variable being the length of time between theinitial CMC encounter and the initial face-to-face encounter.
McKenna and colleagues (2002) found support for theenhancing hypothesis: Participants’ impressions of an opposite-sex individual improved between an initial online chatsession and a subsequent face-to-face interaction, and these
improvements were not found for participants who met faceto-face continuously. However, the time frame of this study
was short: Each interaction lasted for 20 minutes, and theentire study was concluded in one laboratory visit. A series ofstudies by Ramirez and colleagues has replicated thisenhancement pattern when the switch between CMC andOnline Dating
face-to-face interaction (regarding a set of collaborative
tasks) took place after participants had initially communicated by e-mail for 3 weeks (Ramirez & Wang, 2008; Ramirez& Zhang, 2007). However, when the switch took place at 6
weeks, the devaluing pattern started to emerge: Participants’
attraction to their partners after a face-to-face interaction was
lower than in the 3-week condition, and it was about equal to
the no-CMC condition. In other words, face-to-face interactions may produce increases in impression positivity following a brief period of CMC communication, but these benefits
disappear following a longer period of CMC communication(cf. Brym & Lenton, 2003).
Both of these effects are consistent with the hyperpersonal
perspective (Walther, 1996). CMC affords intimate disclosuresand perhaps even the expression of the true self (Bargh et al.,
2002; McKenna et al., 2002), and these benefits of CMC canprovide the foundation for subsequent relationship development
as long as the face-to-face reality check follows somewhat
quickly. However, additional time spent using CMC without areality check may allow strategic self-presentation (on the partof the message sender) and idealization or particularization (onthe part of the perceiver) to continue unabated, and the subsequent face-to-face interaction could produce unpleasant disconfirmation of the perceiver’s overly particular expectations.
Indeed, Ramirez and Wang (2008) provided some evidence thatparticipants’ sense of expectancy violation (e.g., “my partner’s
behavior was not at all expected”) was associated with thedevaluing pattern observed when the face-to-face switch happened after 6 weeks of CMC rather than after 3 weeks. However, it is important to note that in no condition were participants’impressions worse when face-to-face interaction followed CMC
than when participants met face-to-face without first meetingthrough CMC (McKenna et al., 2002; Ramirez & Zhang, 2007).
Thus, for the benefits of CMC to translate to an offline relationship, the modality switch should take place fairly quickly; witha late modality switch, the benefits of CMC can be offset by
unpleasant expectancy disconfirmation.
In short, the nascent literature on modality switching suggests that the CMC services offered by online dating sites may
encourage the development of intimacy and attraction better
than conventional offline dating avenues. However, users
might benefit from making the transition to face-to-face interaction relatively quickly, as any benefits of CMC seem to disappear if interactants rely on CMC for too long as a substitutefor face-to-face interaction.
Why is CMC not a substitute for face-to-face interaction?
Even though CMC can foster intimacy and liking, it does notnecessarily follow that CMC can substitute for face-to-faceinteraction. Indeed, the research reviewed above demonstrated
theoretically meaningful differences between face-to-faceinteractions and text-based information presentations,
including CMC (Eastwick, Finkel, & Eagly, 2011; Ramirez &Zhang, 2007). In other words, there seems to be some face-toface reality that is inaccurately or incompletely communicated37
over CMC. One likely consequence of this disconnect is thatfew people would be willing to start a romantic relationshipwithout having had a face-to-face interaction (Whitty &Carr, 2006). Why would some information about a person be
especially important or apparent in face-to-face interactions?
For one thing, it is easier to misrepresent oneself wheninteracting over CMC than it is when interacting face-to-face.
That is, it may be relatively easy to be deceptive about certain
topics using CMC, and, as noted previously, users of datingsites could be motivated to deceive in the service of impression management. Although people will sometimes misrepresent their occupations, education, or interests online (Caspi &Gorsky, 2006; Whitty, 2002), visible characteristics like age
and appearance are easier to misrepresent over CMC than in
face-to-face interactions (Cornwell & Lundgren, 2001).
Finally, although users may engage in deception for a varietyof reasons (e.g., protecting one’s identity; Utz, 2005), people
report that they are more likely to tell self-serving lies overe-mail than over the phone and in face-to-face interaction(Whitty & Carville, 2008; but see Hancock, Thom-Santelli, &Ritchie, 2004). Partially due to this heightened potential for
deception online, most users seek face-to-face verification
before forming an offline relationship.
Deliberate deception is unlikely, however, to account for
the entire difference between online and offline impression
formation. Even when deception is not an issue, face-to-faceinteraction still conveys information that cannot be gleaned
readily from CMC. In particular, CMC is unlikely to convey
experiential attributes as effectively as face-to-face interactions (Frost et al., 2008). Although CMC can provide greater
depth and texture than online profiles can, even the mostinformation-rich versions of CMC are unlikely to achieve therichness of face-to-face interaction. In a face-to-face interaction, people make gut-level evaluations—momentary, affective reactions to each other—that derive from their own
holistic integration of a potential partner’s innumerable experiential parts (Eastwick, Finkel, & Eagly, 2011). As one online
dater artfully put it: “The whole warm complex animal gestaltof her was unlike anything I could’ve gleaned from e-mails or
jpegs. The difficult love in her voice when she talked about her
father contained a compressed terabyte of information” (Kreider, 2011, para. 16).
Consistent with Asch’s (1946) Gestalt theory of person perception, participants in the Eastwick, Finkel, and Eagly (2011)
research tended to reinterpret the potential partner’s searchable traits to be consistent with their experiential impressions
after the face-to-face interaction. For example, a participant
who did not desire an “outspoken” romantic partner might
have interpreted outspoken to mean “tactless” when examining a potential partner’s profile, but after a positive experience
with this partner, outspoken might be reconstrued as “frank.”
The experiential information provided by the live interaction
caused participants to reinterpret the meaning of the information contained on the profile. In this way, experiential information may drive the relationship-initiation process; in fact, most38
people in Western cultures consider gut-level, emotional evaluations of partners to be an essential component of the decision to initiate and pursue a romantic relationship (Levine,
Sato, Hashimoto, & Verma, 1995; Simpson, Campbell, & Berscheid, 1986).
We hasten to note, however, that the virtual-date paradigm(Frost et al., 2008) may narrow the gap between traditional
forms of CMC and face-to-face interaction by combining chatfeatures, avatars, and a navigable visual environment. Somedating sites (e.g., Weopia) feature virtual dates, and other sites(e.g., SpeedDate) seek to approximate live face-to-face interactions through the use of webcam chats. These newer CMC
methods may offer online daters the best possible opportunity
to assess experiential information about potential romantic
partners over the Internet, even though they are unlikely toachieve the level of nuance and texture emerging from face-toface interaction.
Another reason why CMC is likely to differ from face-toface interaction is that people may evaluate the same attribute,even a searchable trait like physical attractiveness, differently
in CMC than in face-to-face contexts. For example, people
differ in the extent to which the physical attractiveness of apotential partner’s photograph inspires their romantic desire
for that partner, and this individual difference is associatedwith their explicitly reported preference for physical attractiveness. That is, someone with a strong explicit preference forphysical attractiveness is more likely than someone with aweak preference to be romantically inspired by the physicalattractiveness of a potential partner’s profile photo (Wood &Brumbaugh, 2009). However, people also have spontaneous,affectively based preferences (i.e., implicit preferences), andthese implicit preferences tend to be uncorrelated with their
explicit preferences (Eastwick, Eagly, Finkel, & Johnson,2011). That is, participants’ spontaneous affective associations
regarding physically attractive partners do not necessarily correspond to whether they say they ideally want a physicallyattractive partner (an explicit preference). Furthermore,
implicit (but not explicit) preferences for physical attractiveness predict the extent to which participants find physicalattractiveness appealing in face-to-face interaction partners(Eastwick, Eagly, et al., 2011). Implicit preferences are more
likely to be associated with momentary feelings or gut reactions (Gawronski & Bodenhausen, 2011; Ranganath, Smith, &Nosek, 2008), and thus this research suggests that gut-level
feelings about a potential partner take on increased importancein face-to-face interaction relative to CMC contexts.
Thus, the task of an online dater is more difficult than simply assessing searchable traits online, assessing experientialtraits face-to-face, and then summing up the two impressions
to form a romantic judgment. Instead, aspects of a potential
partner that seem promising on a profile or over CMC canleave negative impressions in person, and vice versa. Furthermore, some aspects of one’s romantic impressions are largely
unknowable until a face-to-face interaction has occurred; gutlevel evaluations are crucial and are more likely to be relevant

Finkel et al.
in face-to-face than in CMC contexts (Eastwick, Eagly, et al.,2011). Until CMC procedures can effectively approximate
live interaction, communication services offered by datingsites are unlikely to replace face-to-face interactions as a complete source of romantic impressions.
Communication—conclusion. Taken as a whole, what does
the scholarly literature say about the potential of communication
to yield success in the online dating realm? People can convey
a substantial amount of social information using CMC, andso online profiles, e-mail, and chatting features are all likely
to aid users in the romantic-impression-formation process.
There are two important caveats, however. The first is that thetime frame between the initial CMC interaction and the initial
face-to-face interaction is likely to be important. When thistime frame is short, users’ face-to-face impressions may be
more positive than they would be if no CMC had taken place,
perhaps because CMC facilitates sharing of the true inner self
and provides a strong foundation for a relationship (McKennaet al., 2002; Ramirez & Zhang, 2007). Thus, communication
services offered by dating sites have the potential to produce
superior romantic outcomes relative to conventional offline
dating avenues. However, if the time between the initial CMC
interaction and the initial face-to-face interaction is too long(e.g., 6 weeks in the research of Ramirez & Zhang, 2007),
CMC fails to provide this additional boost, perhaps because
the face-to-face reality fails to live up to people’s exaggerated
or overly particular expectations.
The second caveat is that, despite the ability of CMC toconvey social information, there is something that people
must assess face-to-face before a romantic relationship canbegin. Scholars are still working to identify exactly what thatsomething is, but it appears to reside at the intersection ofexperiential attributes, chemistry, and gut-level evaluations—the myriad verbal and nonverbal factors such as sense ofhumor, rapport, interaction style, holistic impressions, andnonconscious mimicry that determine how comfortably two
people interact and that are better conveyed face-to-face. Partof people’s face-to-face emotional reactions could even be
based on sensual experiences, like olfaction, that cannot begleaned via CMC (e.g., Gangestad & Thornhill, 1998; Zhou
& Chen, 2009). Also, people are less likely to misrepresent
their observable, physical attributes in face-to-face interactions relative to CMC, so there is some wisdom in verifying
these attributes face-to-face before an online romantic relationship becomes intimate.
On the surface, these modern perspectives on relationshipinitiation may seem to echo the cues-filtered-out perspective,
but this similarity is largely cosmetic. Cues-filtered-out proposed that the missing cues in CMC would lead to less social
presence and a reduced ability to form social connections, butthat hypothesis has not held up to empirical scrutiny. But CMC
does indeed “filter out” nonverbal and other cues, and theseother cues affect people’s desire to form a relationship. Thus,
the best conclusion is not that face-to-face interactions offer

Online Dating
better (or worse) potential for forming social connections relative to CMC, but rather that the two forms of communication
offer overlapping yet complementary sets of information, anda person must integrate both into a coherent impression of apotential partner before establishing a romantic relationship.
As the great social psychologist Stanley Milgram (1984, p.
411) put it with astonishing prescience:
But there is a vital limitation to computer-network
affairs: the participants can only go so far on a computer
screen. . . . Eventually the involved parties must go
beyond the computer, meet face-to-face, and deal with
their full complexity as human beings.

Service #3—matching: Does the matchingoffered via online dating yield superior
outcomes?
As noted previously, dating sites vary in the degree to which
they market themselves in terms of the access to potential partners versus in terms of the quality of the matches they provide.
Sites that emphasize access position themselves as relationshipsupermarkets, where they advertise the range of their wares. In
contrast, sites that emphasize matching services position themselves as real estate agents of love, where they advertise theirexperience and expertise. For those in the market for a newhome, the enormous investment, the wide range of available
properties, and the complexities of evaluating a home drive
many to seek the services of real estate agents, whose knowledge of the inner workings of the market and developed powersof discernment are presumed to offer them insight that mosthome buyers lack. For those in the market for a new relationship, a similar dynamic presumably attracts users to matchingsites. Initiating a new relationship, like buying a home, is amajor investment, and the complexities of evaluating a potential partner are great. Those who sign onto matching sites aretrusting in the expertise of the site to navigate those complexities more effectively than they could on their own. Sites offering matching frequently encourage this trust by featuringexperts and invoking scientific research prominently in theirmarketing materials. In the present section, we compare theprocedures employed by matching sites with the existing literature on predicting and understanding romantic outcomes toevaluate whether there is a strong empirical basis for believingthat these procedures are likely to be successful in matchingpartners who are especially compatible.
Is there a solid empirical or theoretical basis formatching? As observed previously, dating sites, especiallymatching sites, frequently make bold claims about theeffectiveness of their procedures for helping users findrelationship partners with whom they will experience positive

39
romantic outcomes, but the evidence supporting these claims
is insufficient to evaluate their validity. In this section, we
review the relationships literature to evaluate whether it ispossible, in principle, to collect information about two people
who have never met and establish whether they are uniquely
compatible, not only initially but also over the long run—as
marriage partners or even soulmates. Our approach enables
us to circumvent the challenges arising from the proprietarynature of most matching sites’ algorithms. Our goal is not tocritique any specific algorithm but rather to evaluate whether
even an optimal algorithm can account for more than a verysmall amount of the variance in predicting positive romantic
outcomes, especially over the long run.
What are the possible outcomes of successful matching?
Collectively, matching sites promise to facilitate two different
outcomes, and in so doing, they set themselves a high bar for
success. First, they promise to identify partners with whomusers are likely to experience a romantic spark. Second, they
promise that those same partners are individuals with whomusers are likely to experience a satisfying and lasting longterm relationship. The sites themselves rarely distinguishbetween these outcomes, accepting the implicit assumptionthat the more one is attracted to someone initially, the more
likely a long-term relationship with that person is to succeed.
This is not a position that requires much defending, as it
expresses what most laypeople already believe (Simpson
et al., 1986), and, indeed, it is consistent with our definition of“positive romantic outcomes.” However, these two outcomes,
although correlated, are distinguishable, which means thatmatching for initial attraction (e.g., a sexual spark on a firstdate) and matching for long-term relationships (e.g., a longterm, satisfying marriage) may be very different endeavors.
How compelling is the empirical and theoretical basis tosupport successful matching? Among the online dating sitesthat use proprietary algorithms to identify suitable partners,
some explicitly promote their ability to match users for theirpotential to experience an initial romantic attraction to eachother, regardless of whether that initial spark is likely to lead toa successful long-term relationship. For example, Chemistry.
com (2011, para. 3) makes plain that:
We know some of our competitors want you to believe
they hold the formula for who you’re compatible withand who is right for you. We at Chemistry.com knowthat only you know who is right for you. So we make it
fun and easy for you to take the first steps in figuring out
who that is before you meet in person.
In other words, they are promising that their algorithms will
facilitate an easy first step, not a lifetime of happiness. Nevertheless, as we have highlighted earlier, the existing empiricalliterature raises serious questions about whether the initialromantic attraction between two people can be predicted reliably on basis of their responses to online profiles. Indeed,40
what people say they are attracted to imperfectly represents
what they are actually attracted to, and what attracts people toa written profile differs from what attracts them to an individual in person (Eastwick & Finkel, 2008a; Eastwick, Finkel, &Eagly, 2011). Both of these issues contribute to the long-standing difficulty of predicting romantic attraction between strangers (Lykken & Tellegen, 1993).
What we have not yet discussed is research relevant to thesecond possible goal of matching sites: arranging successful
long-term relationships. Whereas Chemistry says to its users“You want to go on great dates” and makes no explicit promise beyond that, sites like eHarmony announce, “We deliver
more than just dates” (eHarmony.com, 2011e, para. 1). The
eHarmony homepage claims that “We’ve conducted years ofextensive research and know what makes relationships last”(eHarmony.com, 2011a, para. 1 under the “Why Join eHarmony” tab). A lasting relationship, not just great dates, is theoutcome that eHarmony promises and is the measure of thesuccess of its algorithm. This promise is more in keepingwith matchmaking services prior to the Internet era, whencouples were expected not necessarily to love each other atfirst sight but rather to live together successfully for the restof their lives.
Is there an empirical basis for predicting the long-term success of intimate relationships? If so, how well does that literature map onto the factors being used as the basis for Internet
matching?
i. How predictable are long-term romantic outcomes? Before
discussing the specifics of what predicts long-term romantic
outcomes, we must first consider the extent to which long-termromantic outcomes are predictable at all. Matching sites likeeHarmony strongly imply that the likelihood of any two people
having a successful intimate relationship is knowable in advance.
In their pursuit of this goal, matching sites are standing on theshoulders of giants. Predicting the long-term success or failureof intimate relationships, and marriages in particular, has beencalled the “Holy Grail” of scientific research on relationships(R. E. Heyman & Slep, 2001, p. 473) and has been an explicit
goal of this research since the first studies of marriage wereconducted in the 1930s and 1940s (Adams, 1946; Burgess &Cottrell, 1939; Terman, 1938, Terman, 1948). Only in the pastfew decades, however, have researchers claimed to have madesubstantial progress toward this goal.
In research that received widespread media attention,
Buehlman, Gottman, and Katz (1992) reported that they could,
on the basis of interviewing a married couple and observing a
relatively brief interaction, predict with 94% accuracy whether
or not that couple would divorce over the subsequent 3 years.
Later work with newlyweds reported comparable levels of
accuracy (greater than 80%) predicting divorce over the first 6
years of the marriage (Gottman, Coan, Carrere, & Swanson,
1998). Longitudinal studies by other research teams havereported comparably high levels of accuracy in predicting
marital outcomes (e.g., Rogge, Bradbury, Hahlweg, Engl, &Thurmaier, 2006).

Finkel et al.
Such findings might be taken as encouragement for thedevelopers of matching algorithms, as they suggest the existence of a set of variables that, if they could be measured inadvance, might distinguish between couples more or lesslikely to form successful long-term relationships. However,there are two important reasons that the results of longitudinal
prediction studies of married couples may not provide a strong
basis for attempts to match unacquainted strangers.
First, in a trenchant critique of this literature, R. E. Heyman
and Slep (2001) explained that none of the marriage prediction
studies actually predicts anything. All of these studies use thesame general approach: They assess couples on a wide rangeof variables, follow them for a period of time, determine who
divorces and who does not, and then use a computer program
to identify the combination of variables assessed at baselinethat best accounts for group membership at follow-up. This is
not prediction but postdiction, and it is a perfectly valid way ofidentifying variables that might be important for understanding how relationships develop over time. The true accuracy ofthe prediction, however, can only be established by crossvalidating the results—that is, taking the estimates obtained
from postdiction in one sample and using them to predict outcomes in an independent sample. Longitudinal studies of marriage never do this, but R. E. Heyman and Slep (2001) drewfrom cross-sectional survey data to illustrate how cross-validation affects the accuracy of a predictive algorithm. In theirdata, their ability to account for whether or not an individual
had experienced a divorce dropped substantially (from 65% to21%) when estimates derived from one half of their sample
were applied to the other half. This analysis raises concerns
about the extent to which analyses of known outcomes fromany one sample can support specific predictions about
unknown outcomes in a separate sample, yet this is precisely
what any legitimate matching algorithm would have to do.
A second reason that even the best longitudinal marital
research may not offer much support for the validity of matching algorithms is that, even in a study in which the ability to
predict where most relationships end up is relatively high, theability to account for specific outcomes can be far lower. The
problem, as R. E. Heyman and Slep (2001, p. 474) point out,is that “the predictive value of any measuring device will be
low whenever prevalence itself is low.” In the samples examined in research on married couples, divorce tends to have a
low prevalence, and this can result in a highly accurate overallmodel that still does not identify the divorcing couples well.
To illustrate this point, consider research attempting to accountfor divorce in a population in which the divorce rate is 7% per
year. In a random sample of 100 couples from this population,
7 of them are expected to divorce annually. If, at the beginningof the year, a researcher simply predicts that all 100 of those
couples will remain intact, the researcher will be correct for
93% of the sample—a highly accurate prediction even though
the researcher has not the slightest ability to identify the specific couples who divorce. For dating sites, however, the situation is reversed; that is, most matched couples will fail toOnline Dating
connect. A dating site could therefore develop a highly predictive algorithm if it did no more than prevent pairings that are
unlikely to succeed, as these constitute the majority of possible pairings. What matching sites care about most, however, isthe outcome that is least likely to happen—successfully developing a relationship—and this is the outcome that is most difficult to predict. The existing literature does not inspire
confidence in the ability of even the most accurate predictive
algorithms to account for low-prevalence events.
Questioning the predictability of romantic outcomes does
not undercut the considerable advances that relationship science has made in understanding the forces that contribute to
the success or failure of relationships. As we will elaborate
shortly, many of the important forces that contribute to romantic outcomes are well-established. The point of the preceding
analysis, however, is to emphasize that understanding and predicting are two different goals, and success at one does notnecessarily imply success at the other. By analogy, we mightcompare the understanding and prediction of romantic outcomes to attempts to understand and predict the stock market.
Although economists know a great deal about how the stock
market behaves and why, attempts to predict the behavior of
the market at a specific point in the future have limited accuracy. Relationship scientists, like economists, have made great
strides in identifying the determinants of their outcome ofinterest: relationship success or failure. Matching sites, however, are in the business of predicting the future success or
failure of a particular couple, and there may be inherent limits
to the ability of available scientific research to support that
goal.
ii. On what basis can we predict romantic outcomes? To
the extent that romantic outcomes are at all predictable inadvance, on what basis might those predictions be made?
Theories of intimate relationships and marriage have generally
highlighted three broad classes of variables as reasons why
these relationships succeed or fail (e.g., Huston, 2000; Karney& Bradbury, 1995). First, relationships are naturally affectedby the individual characteristics of the partners (e.g., Kelly &Conley, 1987; Robins, Caspi, & Moffitt, 2000). These include
all of the relatively stable aspects of two people, including
not only their personalities but also their personal histories,
attitudes, values, and backgrounds. Second, relationships
develop as a function of the quality of the interactions between
the partners (e.g., Gottman, 1994; Johnson et al., 2005).
These include all of the ways that couples communicate,resolve problems, and support each other, as well as theiremotional reactions to and interpretations of these behaviors.
Third, relationships can be constrained or facilitated by thecircumstances surrounding the couple (e.g., Conger, Rueter,& Elder, 1999; A. Thompson & Bolger, 1999). These include
chronic and acute sources of stress and support, as well as theneighborhoods, cultures, and social networks within which
couples are embedded.
These three broad classes of variables are not equallyassessable by the methods adopted by matching sites. For

41
example, whereas dyadic interactions play a central andexplicitly causal role in several of the most influential theories
of relationship development and treatment (e.g., social learning theories; N. S. Jacobson & Christensen, 1996; Weiss,1978), and whereas the longitudinal studies of Gottman andhis colleagues (e.g., Buehlman et al., 1992; Gottman et al.,1998) based their predictions primarily on observations ofcouples’ interactions and behaviors, this entire class of variables is necessarily excluded from consideration by the algorithms used by extant matching sites. Matching algorithms aredesigned to predict romantic outcomes between individuals
who have not yet met. Having never interacted, these potential
couples therefore cannot provide interaction data on which tobase a prediction about their future success or failure, despite
the central importance of these variables in all contemporarymodels of relationships.
In contrast to their interactions, some aspects of the circumstances of a couple can be assessed before the partners havemet. Socioeconomic status, for example, is a powerful predictor of romantic outcomes, such that divorce rates are nearly
twice as high in low-income neighborhoods as in affluent
neighborhoods (Bramlett & Mosher, 2002). Similarly, couples
under financial stress have more trouble resolving conflicts
effectively and, as a consequence, experience lower relationship satisfaction, compared to couples who are more financially stable (Conger & Conger, 2008). These concrete aspects
of individuals’ environments may be measured in advance oftwo people meeting and could be incorporated into an algorithm to predict, for example, that users from poorer neighborhoods, or users experiencing greater economic stress, will
have a more difficult time forming and maintaining a lasting
intimate relationship. Matchmaking sites do not appear toemphasize these sorts of economic issues in their algorithms(see our previous review of major sites’ key matching principles), and, even if they did, it is not clear how they would do
so. Moreover, because matching sites frequently charge substantial membership fees, it seems likely that the clientele forthese sites draws disproportionately from among the more
affluent, for whom financial stress is less acute and therefore
may be less predictive.
An element of individuals’ circumstances that matchingsites surely do account for is geography. One of the earliest
and most reliable findings in research on relationships is thatlasting relationships are more likely to form between people
who live near each other—people for whom it is relatively
easy to meet in person and interact (Festinger, Schachter, &Back, 1950). Matching sites have integrated this finding into
their designs, and all allow users to specify the geographic
region within which they would be willing to search for apartner.
Yet, from the perspective of predicting romantic outcomes,
data on many of the most important elements of a potential
couple’s circumstances are inaccessible in light of matchingsites’ data collection procedures. Classic and still-influential
models of functioning in relationships and families (e.g., crisis

42
theory; Hill, 1949) emphasize that romantic outcomes arestrongly determined by the extent to which couples experiencechronic and acute stress and by the way couples cope withstressors when they arise. Although it is possible for couples tocope effectively with stress and thus grow closer, stress generally increases opportunities for conflict even as it decreases a
couple’s capacity to resolve conflict effectively (Neff & Karney, 2009). As a consequence, the experience of stress is areliable and significant predictor of negative romantic outcomes across a wide range of domains. For example, couples
coping with unemployment (Charles & Stephens, 2004), infertility (Monga, Alexandrescu, Katz, Stein, & Ganiats, 2004),
chronic illness (Teachman, 2010), incarceration (Lopoo &Western, 2005), or natural disasters (Cohan & Cole, 2002) areall at higher risk of experiencing declines in the quality andlongevity of their relationships.
For a matching Web site attempting to predict the outcomeof an intimate relationship in advance, anticipating the rolethat stress will play in the development of that relationship islimited for two reasons. First, many of the stressors that willaffect the success or failure of a relationship arise from factors
entirely outside of the partners’ control and are unknowable inadvance. A flood or a hurricane, an economic downturn or theclosing of a factory, a sudden illness in a child or family member—all of these events may tax a couple’s resources in waysthat change the relationship, but their occurrence cannot be
predicted. The presence of such events in couples’ lives places
an upper limit on the predictability of their romantic outcomes.
Second, matching sites tend not to ask their users to report on
the stressors they already or will likely experience. Generally
absent from the assessments we reviewed, for example, were
any questions about chronic illnesses or risk factors for developing chronic disease in the future (e.g., a history of substanceabuse or clinical depression). Similarly absent were questions
directly assessing financial stability, employment stability, or
the likelihood of career advancement. Thus, even those aspectsof stress that might be knowable are being overlooked, despite
the presence of an extensive literature pointing to their importance for understanding relationship success and failure.
So, within the broad landscape of variables known to affectlong-term romantic outcomes, on what information can matching sites assess and base their predictions? Only qualities of
the individual, and this of course is where the assessments ofmatching sites focus most of their attention. Indeed, matchingsites necessarily focus on qualities of individuals, because
these qualities are what the sites are in the best position toassess before a couple’s relationship begins.
To illustrate the contrast between what matching sites canassess and what they cannot, Figure 3 assembles the major
themes of models predicting initial romantic attraction andlong-term romantic outcomes into a single model, an extension of the vulnerability-stress-adaptation model of marriage(Karney & Bradbury, 1995). The model begins with two unacquainted individuals, Partner A and Partner B. Matching sites
assume that, with adequate knowledge about these two

Finkel et al.
individuals, an algorithm can be developed to predict whether
these individuals are likely to enjoy long-term relationshipquality (i.e., fulfillment in the relationship) and, consequently,
long-term relationship longevity (i.e., a relationship that persists over time). This assumption is depicted by the dotted
paths in the figure. All the other paths in the figure, however,represent themes and paths of influence that matching algorithms necessarily overlook—that is, all of the processes andexperiences that begin only after two unacquainted individuals
have interacted for the first time. The figure thus makes clearthat the qualities of two individuals are likely to account for
only a small fraction of the variance in the long-term outcomesof a relationship. Even if the initial attraction between two
people could be predicted accurately before they met, thecourse of their relationship would still be shaped by elements(i.e., external or uncontrollable events and individual change
and maturity) that emerge over time and cannot be predicted inadvance. To the extent that these paths of the model accountfor important variance in relationship success or failure—anddecades of empirical research indicate that they do—then theneglect of those factors imposes another limit on the ability of
any matching site to predict in advance which relationships
will form and which will last.
iii. How well do partners’ individual qualities predict their romantic
outcomes? Given the inherent limitations of an approach tomatching that excludes many of the elements most strongly
associated with romantic outcomes, we may nevertheless ask
how effectively long-term romantic outcomes can be predictedby the individual characteristics that matching sites do assess.
Models to explain how the qualities of two individuals may
account for the outcomes of a relationship between themgenerally adopt one of two perspectives, which we will callthe relationship aptitude perspective and the compatibility
perspective.
The premise of the relationship aptitude perspective is that,
due to their traits, preferences, and personal history, some people are likely to have better relationships than others. The earliest research on marriage adopted this perspective and was
explicitly designed to identify specific personality traits, values, and personal experiences that made people more or lesslikely to have successful marriages (e.g., Burgess & Cottrell,
1939; Terman, 1938). Research on adult attachment orientations (e.g., Fraley & Waller, 1998; Hazan & Shaver, 1987)
continues in this tradition by suggesting that individuals possess relatively stable mental models of intimacy that they
develop through interactions with their primary caregivers inearly childhood. Attachment theory (Bowlby, 1969, 1973,1980) suggests that those with sensitive, responsive caregivers
are likely to develop secure models of attachment, which facilitate closeness with others in adulthood. In contrast, those with
absent or unresponsive caregivers are likely to develop insecure models of attachment, which lead to anxiety about
depending on others or avoidance of intimacy altogether in
adulthood. What this perspective and all relationship aptitude
models have in common is the idea that the capacity forOnline Dating

43

Partner A
External or
Uncontrollable
Events

InitialCommunication
and Interaction

InitialAttraction

Adapting, Coping,and Interacting
Over TimeLong-Term
Relationship
QualityLong-Term
relationshipLongevity

Individual
Change andMaturity
Partner B
Time
Fig. 3. A model of long-term romantic outcomes. The dotted paths represent the models implicit in matchmaking algorithms, which suggest thatcharacteristics of two individuals measured prior to their meeting can directly predict the long-term outcomes of their relationship. The solid
paths represent all of the intermediate steps and external sources of influence known to predict long-term relationship outcomes in addition to
the characteristics of the two partners. To the extent that any of the constructs absent from matchmaking models are imperfectly related to or are
independent of partners’ individual qualities, then qualities of two individuals that can be measured before they meet are likely to account for only asmall fraction of the variance in the long-term outcomes of a relationship.

successful intimate relationships is a relatively stable qualityof an individual, regardless of who that individual’s partner is.
An enormous body of research supports this view. For
example, those early marriage studies successfully identified
personality traits that were associated with more or less satisfying or enduring marriages over time (e.g., Adams, 1946;
Terman, 1938). Over the subsequent decades, as theories of
personality crystallized around the five-factor model (McCrae& Costa, 1997), research has examined each of the five factors
for its cross-sectional and longitudinal associations with relationship processes and outcomes and has confirmed that mostof them are associated with romantic outcomes in some way(Malouff, Thorsteinsson, Schutte, Bhullar, & Rooke, 2010;
McNulty, in press). By far, the most consistent and extensive
evidence has accumulated for neuroticism, defined as the stable tendency to experience negative emotions (Watson &Clark, 1984). In all of these studies, higher levels of neuroticism in either partner are associated with lower relationshipsatisfaction cross-sectionally (e.g., Bouchard, Lussier, & Sabourin, 1999) and declining satisfaction and greater risk of
dissolution over time (e.g., Karney & Bradbury, 1997). Moreover, personality traits account for people’s tendency to experience similar outcomes across different relationships (Robins,
Caspi, & Moffitt, 2002), strong support for the idea that sometraits are associated with less success in relationships regardless of the specific relationship partner. In a compellingdemonstration of this effect, the neuroticism of husbands andwives not only predicted their marital outcomes over 45 years
later, but this effect swamped the effects of almost all otherindividual characteristics that they had measured (Kelly &Conley, 1987).
Aside from personality traits, other individual differences
have also been identified as significant predictors of romantic
outcomes. The predictions of attachment theory have beenborne out in hundreds of studies showing that relatively stableorientations toward closeness and intimacy are reliably associated with outcomes in adult relationships (e.g., Holland,
Fraley, & Roisman, 2011), even when attachment orientations
are measured far in advance of the formation of a relationship(Roisman, Collins, Sroufe, & Egeland, 2005). Specific childhood experiences have been linked to adult romantic outcomesas well. For example, children whose parents were unhappily
married or divorced are at greater risk for growing up to have
unhappy relationships themselves (Amato & Booth, 2001;
Feng, Giarrusso, Bengtson, & Frye, 1999). A history of psychopathology (Gotlib, Lewinsohn, & Seeley, 1998) and childhood experiences of abuse (Cherlin, Burton, Hurt, & Purvin,2004) are also powerfully associated with greater difficulties
forming and maintaining relationships in adulthood.
As a whole, research linking relatively stable individual
differences to the long-term success or failure of relationships has several important implications for the prospects of44
matching over the Internet. First, although the effects of individual differences are reliable and significant, they are generally small to moderate in size. In this literature, parental
divorce, for example, has proven to be one of the variables
with the highest impact, roughly doubling a child’s risk forrelationship difficulties in adulthood (McLanahan & Sandefur,
1994; Simons, 1996). Still, this leaves most of the variance inadult romantic outcomes unexplained. Similarly, as reliable aspersonality traits have been as predictors of romantic outcomes, a meta-analysis found that, across several longitudinalstudies, even neuroticism, the personality trait with the largest
effects on relationships, generally accounts for less than 5% of
the variance in relationship satisfaction over time (Karney &Bradbury, 1995). Thus, although the characteristics of individuals matter to their relationships, history is not destiny, andfor similar reasons neither is personality.
Second, this research nevertheless offers some empiricalbasis for making predictions about long-term romantic outcomes based on qualities of two individuals who have not yet
met. Many of the individual differences that have been identified as predictors of romantic outcomes are easily and accurately assessed through self-reports. This ability to measurepersonality and personal history suggests a concrete, empirically based strategy for improving the likelihood of successful
matching: Eliminate from the pool of potential partners thoseindividuals most at risk for experiencing distressing or unstable relationships. By setting a high threshold for membership,subscribers to a site might be guaranteed a certain minimum
level of quality in those they meet on the site and thereby anacceptable level of risk for relationships formed between any
two individuals in the pool. Meeting potential partners throughsuch a site would be analogous to joining an exclusive club,
something that people have historically paid considerable
sums to do.
Some online dating and matching sites are explicitly doing
something like this already, turning away some of those whowish to subscribe because they fail to meet minimum criteria.
For example, as we noted earlier, eHarmony rejects relationship seekers who are not judged to be “emotionally healthy”
according to their criteria (eHarmony.com, 2011e). In response
to a lawsuit, Match began screening out users who appear onregistries of sex offenders (Stone, 2011). To the extent thatscreening in these ways eliminates users most likely to haverelationship problems, these sites may be performing a valuable service, as most people cannot administer similar screensto the people they meet offline. However, this service also has
several costs. First, because the individual-level markers ofrisk are far from perfect, such screens may eliminate somepeople unfairly and certainly may let through others who haverisk factors that were not measured. Second, making this strategy public may motivate potential users to misrepresent themselves on the matching survey. Third, whereas elevating thequality of the dating pool may reduce the likelihood of grossly
inappropriate matches, by itself this strategy falls short of thepromise to match people who are uniquely suited to one

Finkel et al.
another. Fourth, this strategy may be harmful to those users
whom the matching site rejects as unmatchable.
iv. How well does similarity between partners’ individual
qualities predict their romantic outcomes? The second groupof models that link qualities of individuals to the outcomesof their relationships adopts the compatibility perspective.
The compatibility perspective assumes that success in a
relationship is a function of the unique combination of two
individuals’ qualities. Whereas the relationship aptitude
perspective assumes that some people are more versus lessequipped for successful relationships regardless of theirpartner, the compatibility perspective assumes that there is asuitable partner for almost everyone but that the same partner
who is right for one person might be wrong for another.
The relationship aptitude perspective and the compatibility
perspective are not mutually exclusive. There can be traits
that make a person more versus less likely to succeed atrelationships generally, and at the same time there can be
combinations of individual characteristics that uniquely
predict the likelihood that two people will have a successful
relationship. Indeed, widely available statistical techniques
allow researchers to tease apart these effects within thesame data (Kenny, Kashy, & Cook, 2006). Nevertheless, itis the compatibility perspective that most directly expresses
what contemporary matching sites promise their users. For
example, eHarmony famously promises that “every matchis prescreened for deep compatibility with you across 29
dimensions” (eHarmony.com, 2011a). The implication is thatthe matches provided by the site are not just people who arelikely to have good relationships but are those people who arelikely to have a good relationship with you.
On what basis might the specific compatibility of two individuals be determined? As discussed previously and summarized in Table 2, by far the most frequently cited basis forcompatibility is similarity. If birds of a feather are indeed happier when they flock together, one of the simplest things amatching site can do to create happy relationships is pair
together partners who are like each other. The attractiveness ofsimilarity was one of the first hypotheses studied in research oninterpersonal relationships (Newcomb, 1956), and it has sincereceived consistent support from research showing thatmatched strangers do in fact like each other more to the extent
that they have (or at least believe that they have) more things incommon (e.g., Byrne, 1961; Byrne, Ervin, & Lamberth, 1970).
The widespread evidence for homogamy—the frequently
observed tendency of spouses and relationship partners to be
more similar to each other than to randomly matched strangers
from the same population—has been taken as further support
for the benefits of similarity (Blossfeld, 2009; Luo & Klohnen,
2005; Mare, 1991; Watson et al., 2004).
The relevant question for matching sites, however, goesbeyond homogamy. For a matching algorithm based on similarity to predict long-term romantic outcomes, it must be demonstrated not merely that people are attracted to and tend tomatch up with similar others but that a greater degree ofOnline Dating
similarity between two people predicts more satisfying andlonger-lasting relationships over time. This is not an easyquestion to answer, for several reasons.
First, measuring similarity is surprisingly complicated. The
perception of being similar to a partner is easy to measure, andindeed that perception is strongly associated with initial attraction and relationship satisfaction (e.g., Lutz-Zois, Bradley,
Mihalik, & Moorman-Eavers, 2006; Montoya, Horton, &Kirchner, 2008). But perceptions of similarity are an imperfectproxy for actual similarity, and indeed may reflect relationshipsatisfaction rather than contribute to it (Morry, Kito, & Ortiz,2011). In any case, perceived similarity cannot exist between
two individuals who have yet to meet. Instead, to evaluate similarity, matchmakers must rely on either the difference approachor the profile approach. The difference approach is morestraightforward: To estimate the similarity between two people
on some dimension, researchers compare their scores on that
dimension and consider smaller absolute differences to be signsof greater similarity (e.g., Luo & Klohnen, 2005). Among thelimits of this approach, however, is the fact that, across multiple
dimensions of comparison, positive and negative differences
between individuals on different dimensions can cancel eachother out, producing misleading results. To avoid this problem,
researchers interested in comparing individuals on multiple
dimensions at once typically rely on the profile approach, whichcalculates the correlation between the scores of two individuals
across all of the dimensions assessed and treats a strong correlation as a sign of greater similarity (e.g., Glicksohn & Golan,
2001). The profile approach also has its limitations: The estimated similarity between two individuals will be inflated when
their responses resemble the responses that most people typically give (Kenny & Acitelli, 1994). There are multiple ways tocorrect for this problem (Furr, 2008), but these corrections havebeen used sporadically in published studies. Perhaps as a resultof the general inconsistency in the way that similarity has been
operationalized, the results of research on the implications ofsimilarity for romantic outcomes are “inconsistent and difficult
to interpret” (Watson et al., 2004, p. 1035).
The second major problem for research on similarity andrelationships is a lack of consensus on what dimensions ofsimilarity are likely to matter most. Because human beings arecomplicated and multifaceted, two individuals who resemble
each other in one way (e.g., they both love big parties) are
unlikely to resemble each other in every way (political conservatism, a taste for Ethiopian food, ethnic background, openness to new experiences, etc.). The idea that similarity ingeneral may facilitate successful relationships says nothing at
all about which dimensions of similarity should be favored
over others. In the absence of a clear theoretical direction,
research on the implications of similarity has examined many
possible ways that partners can be similar, obtaining varying
results for different types of similarity.
Perhaps the clearest findings in this literature involve
matching on demographic variables. For example, analyses ofcensus data reveal that marriages between spouses of the same45
race or ethnicity have a lower divorce rate after 10 years thaninterracial or interethnic couples (31% risk vs. 41% risk;
Bramlett & Mosher, 2002). In comparably large and representative samples, being of the same religious denomination has
also been associated with longer-lasting marriages (Heaton &Pratt, 1990), and being similar in education, parental wealth,and earned income has been associated with happier marriages(Weisfeld, Russell, Weisfeld, & Wells, 1992). From the perspective of matching, however, such findings are not especially useful because (a) people who meet their spouse throughmore conventional approaches to dating tend to marry within
their own demographic constellation in the first place and (b)matching on the basis of demographic similarity does little tonarrow the range of potential partners for any one individual.
That is, knowing that a person who is White, college educated,
and Catholic is likely to be happier with another person who isWhite, college educated, and Catholic still leaves an unmanageable number of partners from which to choose. The rise ofdating sites that cater specifically to a particular demographic
niche (e.g., AveMariaSingles) only exacerbates this problem.
For this reason, discussions of matching tend to focus lesson broad social groupings and more on psychological variables like personality. The underlying hypothesis is that individuals with similar styles of interacting with the world shouldfind it easier to get along and so should experience more successful intimate relationships. Despite the appeal of this idea,
however, the results of numerous studies examining it havebeen, on the whole, weak and contradictory. Several studies
have reported positive associations between profile similarity,
based upon multiple dimensions of personality, and concurrent
or future relationship satisfaction (e.g., Decuyper, De Bolle, &De Fruyt, in press; G. C. Gonzaga, Campos, & Bradbury,
2007; G. C. Gonzaga, Carter, & Buckwalter, 2010). But otherstudies have found that, after controlling for the important
main effects of each partner’s personalities, the similarity
between their personalities had only a negligible additional
effect on their romantic outcomes (Glicksohn & Golan, 2001;
Lewak, Wakefield, & Briggs, 1985; Luo et al., 2008; Luo &Klohnen, 2005; Robins et al., 2000; Russell & Wells, 1991; N.
D. Tidwell, Eastwick, & Finkel, in press; Watson et al., 2004).
One of the most rigorous recent investigations of theseeffects puts the role of personality similarity into perspective(Dyrenforth, Kashy, Donnellan, & Lucas, 2010). In analysesof three large, nationally representative datasets from Australia, the United Kingdom, and Germany (total N = 23,250),
each partner’s personality accounted for approximately 6% of
the variance in his or her own relationship satisfaction andbetween 1% and 3% of the variance in the other partner’s relationship satisfaction. Controlling for these effects and using
the most stringent methods for estimating similarity effects(i.e., profile matching of standardized scores), the similarity
between partners’ personalities accounted for an additional
0.5% of the variance in romantic outcomes, leading the authors
to conclude that “actor and partner effects of personality are
more robust and consistent than similarity effects when

46
predicting marital and life satisfaction” (Dyrenforth et al.,2010, p. 701). A meta-analysis of 313 studies that examined
actual and perceived similarity in laboratory studies and studies of real couples reached a similar conclusion: Althoughsimilarity predicted attraction in the laboratory studies, theeffect of actual similarity on satisfaction in existing relationships was not significantly different from zero (Montoya et al.,2008).
The inconsistent effects of personality similarity are not sosurprising in light of the fact that some personality traits are
distinctly negative. As noted earlier, the personality dimension
with the strongest and most consistent direct effects on romantic outcomes is neuroticism. Given the abundant evidence thathigher levels of neuroticism are associated with worse relationships, it is hard to imagine that similarity on this dimension (e.g., a couple in which both partners score high) would
be preferable to a couple in which the total level of neuroticism across the partners was lower, regardless of similarity. In
fact, with respect to negatively valenced personality traits,
similarity between partners is clearly bad for relationships. For
example, in research on dyadic interactions, similarity on
introversion/extraversion predicts more effective communication, but similarity on disagreeableness predicts less effective
communication, even after controlling for the main effects ofeach partner’s personality (Cuperman & Ickes, 2009). In married couples, each spouse’s depressive symptoms are consistently associated with lower marital satisfaction (e.g., Davila,
Karney, Hall, & Bradbury, 2003), but after controlling forthese effects, marriages in which both partners are depressedfare even worse (Whisman, Uebelacker, & Weinstock, 2004).
On the flipside, the summed total of two partners’ self-control,
not their similarity on this dimension, predicts elevated relationship quality (Vohs, Finkenauer, & Baumeister, 2011).
Thus, with respect to matching on personality, the relationshipaptitude perspective seems to have more support than the compatibility perspective. The implication for relationship seekers
and matchmakers is that finding a partner with a personality
conducive to relationships is more likely to promote successful outcomes than is finding a partner with a personality similar to one’s own.
The promise of similarity as a basis for successful relationships is clearer in research on attitudes and values. Whereas
personality traits like neuroticism are “vertical attributes,” inthat partners with difficult personalities should be less attractive to everyone, many attitudes and values are “horizontal
attributes,” in that there is nothing inherently positive or negative about them. Most people would prefer partners high invertical attributes like attractiveness, health, and wealth, butthere is nothing universally attractive about horizontal attributes like voting Independent, loving foreign films, or reading
science fiction (for a discussion of this distinction, see Hitsch
et al., 2010a). We would therefore not expect each partner’s
endorsement of any specific attitudes or values to have any
direct effects on the couple’s romantic outcomes, but the similarity between partners on these dimensions could improve

Finkel et al.
their relationship by reducing areas of potential conflict andopening domains of shared interest.
Although the tendency for partners within a couple to share
their important attitudes and values (e.g., preferences, politicalviews, spirituality) is strong and well-documented (e.g.,
D’Onofrio, Eaves, Murrelle, Maes, & Spilka, 1999; Nagoshi,
Johnson, & Honbo, 1992), support for the idea that greater
similarity on attitudes and values benefits relationships
remains weak and inconsistent. Watson et al. (2004), forexample, identified high levels of similarity in political orientation, religiosity, and life values among their sample of newlywed couples, but the degree of similarity on these dimensions
was generally not associated with levels of marital satisfaction. An independent study of newlyweds found that similarity
between spouses’ preferences for how they spend their leisuretime was associated with feelings of love among husbands but
not among wives (Houts, Robins, & Huston, 1996). In a comparable study of longer-married couples, who presumably
experience a wider range of romantic outcomes than newlyweds, similarity in life values was significantly associatedwith husbands’ but not wives’ marital satisfaction, and similarity on attitudes toward family issues was not associated withmarital satisfaction for husbands or wives (Gaunt, 2006). A
study of over 1,000 Chinese couples found stronger results,
estimating that sharing intrinsic values (e.g., the importance oflove, fairness, personal growth) accounted for 27% of the variance in wives’ satisfaction and 28% of the variance in husbands’ satisfaction (Luo et al., 2008), but no other study ofthese issues has obtained similarity effect sizes in this range.
One specific domain in which similar attitudes may beespecially likely to facilitate successful long-term relationships is sex-role traditionalism. A long-term marriage between
a man and a woman requires that they coordinate householdchores, work outside the home, and childcare; all of thesenegotiations can be expected to go more smoothly for couples
who share similar ideas about which partner should do whatsort of work and how decisions within the home should be
made. Indeed, spouses who share similar beliefs about genderroles do report higher satisfaction and lower levels of conflictin their marriages (Houts et al., 1996; Lye & Biblarz, 1993),
and agreement on these issues is associated with more effective communication in observational studies as well (Overall,Sibley, & Tan, 2011).
In summary, what can we say about the empirical basis formatching based on similarity? The picture is mixed. On one
hand, most people do end up paired with partners who strongly
resemble themselves, at least in terms of demography and attitudes. On the other hand, once paired, it remains unclear
whether the degree of similarity within the couple is associated with more versus less successful relationships over time.
In the case of vertical attributes, the main effects of individual
differences greatly outweigh any independent effects of similarity, and for negative attributes like depression, similarity isclearly detrimental. In the case of horizontal attributes like
attitudes and values, similarity seems to be most powerful forOnline Dating
those values most directly associated with coordination withina family, including religion and attitudes toward sex roles.
Thus, as a basis for compatibility in long-term relationships,
similarity may be a beginning, but it is far from being thewhole story or even a large part of the story.
From the perspective of evaluating matching sites, one limitation of this review is that it necessarily focuses on dimensions of similarity that have been examined in publishedempirical research. These dimensions may not map perfectly(if at all) onto the dimensions assessed by matching sites, suchas the 29 dimensions advertised by eHarmony. Yet Lykken andTellegen (1993) explained why research on additional dimensions is unlikely to improve the ability of similarity alone toaccount for the formation of successful long-term relationships. They observed that, even in a world where people benefit from similar partners, people are also likely to tolerate a
range of difference in their partners, and indeed the averagecorrelation between actual partners, although reliably significant, is rarely higher than .50 regardless of the dimension
being assessed. Given that range of acceptable similarity, in an
ideal case in which an individual had 10 completely independent dimensions along which to choose a similar partner, asearch for partners who were within an acceptable range ofsimilarity on all 10 dimensions would not go far in limiting thepool of available mates. In the real world, where the available
dimensions that can be assessed are far from independent andin fact overlap highly within individuals, the ability of similarity matching to identify a uniquely suitable partner is evenmore limited. Describing the hypothetical case of a relationship seeker basing a search entirely on matching for similarity,
the authors concluded as follows: “For our lonely bachelor . . .
the entire literature on spousal similarity would eliminate notmore than half the young women whom he encounters andwill therefore leave him still unmated” (Lykken & Tellegen,1993, p. 59). These authors, however, were writing before thecurrent rise in the popularity of online dating. With the vastly
wider range of potential mates that the Internet offers, the ability of similarity alone to narrow the field is even more
limited.
To address this problem, matching sites could, and probably do, present users with access to only a handful of the possible options identified by their algorithms. Given the various
adverse effects, discussed previously, of exposure to a large
number of options, limiting the dating pool in this way is perhaps a wise decision. However, limiting the pool of compatible partners in a relatively arbitrary or random manner does
not represent any great advance beyond the ways in whichconventional offline dating has always limited the pool ofpotential partners.
v. How well does complementarity between partners’ individual
qualities predict their romantic outcomes? As noted earlier,similarity is by far the most frequently discussed and mostthoroughly researched basis for compatibility in researchon romantic relationships. But it is not the only basis. An
alternative perspective on compatibility suggests that, at least47
on some dimensions, couples benefit from their differences—from complementarity. In contrast to the idea that birds of afeather may flock together, the complementarity perspective
suggests that, at least in some cases, opposites attract. Morespecifically, the sociologist Robert Winch (1958) proposed
that, instead of being attracted to people like ourselves, weare actually attracted to people who possess valued personalqualities that we lack. Many people find this idea appealing, as
it seems to describe successful couples in which one extravert
partner is the life of the party while the introvert partner looks
on, or in which one hard-headed, practical partner pays thebills while the free-spirited, nurturing partner cares for thechildren. Indeed, as discussed previously and in Table 2,complementarity has been explicitly named as informing atleast part of the matching algorithms used by some matchingsites (e.g., PerfectMatch, Chemistry), and it has been featured
in recent popular books on romantic attraction and mateselection (Fisher, 2009).
Yet, despite decades of continued interest in complementary
personalities, empirical evidence that differences between partners benefit relationships has been even harder to come by thanevidence for the benefits of similarity (e.g., Klohnen & Luo,
2003; Markey & Markey, 2007). For example, there is no evidence that introverts are uniquely attracted to extroverts (Hendrick & Brown, 1971). More generally, there is no evidence
that people are attracted to individuals with personality traits
that they perceive to be lacking in themselves (Klohnen &Mendelsohn, 1998; Till & Freedman, 1978). Nor is there any
evidence that couples are more satisfied when they have complementary attitudes (Aube & Koestner, 1995), interests (Houts
et al., 1996), or spending habits (Rick, Small, & Finkel, 2011).
Even research on sex roles fails to find that more psychologically masculine individuals benefit from more psychologicallyfeminine partners (in fact, everyone benefits from more feminine partners; Antill, 1983). Research on complementarity has
been reviewed repeatedly, and in every case the idea that complementary personalities might provide a basis for compatibility in romantic relationships has been dismissed as failing todescribe the available data (e.g., White & Hatcher, 1984).
One reason that ideas about the benefits of complementarity
for relationships persist despite decades of evidence to the contrary is that, within a relationship, two people who are basicallysimilar to each other can adopt complementary roles (Levinger,1986). For example, in randomly matched pairs of unacquainted
females, those pairs in which one partner adopts a more dominant role than the other perform their assigned tasks more effectively (Dryer & Horowitz, 1997; Markey, Lowmaster, & Eichler,2010). When the alternative is jockeying for position, it makes
sense to accommodate our partners in this way. Indeed, whencouples are experimentally placed in a situation that might
invoke competition within the relationship, they tend to perceive more complementarity than when they are not in a competitive situation (Beach, Whitaker, Jones, & Tesser, 2001).
Still, the complementarity in all of these studies is situational,
not dispositional. None of this is of any use to matching

48
algorithms, which must evaluate compatibility between two
people before they have entered a situation together.
Some scholars have drawn from research on the benefits ofgenetic diversity in other species (Bateson, 1983) to proposethat a degree of genetic dissimilarity should be sought in a
potential mate among humans as well (e.g., Penn, 2002). The
argument is that, just as inbreeding increases the risk of offspring born with severe recessive disorders, a preference foroutbreeding, or selecting mates on the basis of their genetic
diversity, should promote healthy offspring and should therefore be selected for within a population. Within humans, thissort of argument has been applied specifically to research on aset of genes central to immune functioning called the major
histocompatibility complex (MHC). MHC genes are more
effective at producing antigens when they include more polymorphisms—that is, when an individual receives different
forms of the genes from each parent. If infants receiving different copies of the genes therefore have healthier immune
systems and are more likely to survive, then selection pressures should have led adults to identify and prefer to mate withindividuals whose MHC genotype is different from their own(Grob, Knapp, Martin, & Anzenberger, 1998). In fact, several
studies to test this idea have found that, when asked to smell
t-shirts worn by possible partners who are MHC-similar or
MHC-dissimilar, male and female research participants prefer
the odor of the MHC-dissimilar partners (Thornhill et al.,
2003; Wedekind, Seebeck, Bettens, & Paepke, 1995).
On the basis of these and related findings, the matching site
ScientificMatch, which apparently shut down as we weremaking the final edits to this article, offered to test genetic
material. This site promised that, after their algorithm identified MHC-dissimilar mates, there would be an increased
chance that users would “love the natural body fragrance ofyour matches” (ScientificMatch.com, 2011). It was an enticing promise, but recent reviews of the research on MHC in
humans suggest that the benefits of MHC-dissimilarity are far
from simple or clear. To date, evidence that real couples actually choose each other on the basis of MHC-dissimilarity,
which would be expected if humans have evolved to prefer for
genetically dissimilar mates, has been inconsistent (Havlicek& Roberts, 2009), although one study found that MHCdissimilarity is associated with greater sexual attraction
between partners (Garver-Apgar, Gangestad, Thornhill,
Miller, & Olp, 2006). Moreover, although men and women do
prefer the odors of MHC-dissimilar others, it appears that they
prefer the faces of MHC-similar others (Roberts & Little,
2008; Roberts et al., 2005). As will surprise no one, faces also
play an enormous role in mate preferences (Little & Perrett,2002). Thus, the current state of research on genetics and mateselection suggests that matching based on MHC-dissimilarity
is, at best, premature.
Matching—conclusion. A review of the empirical literatureon relationship functioning suggests that it is virtually
impossible to succeed at the task many matching sites have

Finkel et al.
set for themselves. On one hand, they can probably discern
which people are likely to be relatively poor companions inmost relationships by assessing stable individual differences
like neuroticism and a history of substance abuse. Evidence
that some people are better at sustaining intimacy thanothers, regardless of their partner, is strong and unequivocal.
Assessing these characteristics can potentially act as ascreening device, and this is a service that online dating sites
are likely to perform far more efficiently and effectively than
any individual can.
On the other hand, this screening service is far less thancontemporary matching sites promise their users. Matchingsites promise to identify potential mates who are uniquely
compatible with their users, and this is a promise unlikely tohave a great deal of empirical support, for multiple reasons.
First, the promise assumes that compatibility effects on romantic outcomes are relatively large and that the bases of compatibility in relationships are well established. Neither of theseassumptions is true. Second, and more troubling for the potential of Internet matchmaking, research on intimate relationships suggests that there are inherent limits to how well the
success of a relationship between two individuals can be predicted in advance of their awareness of each other. Many of
the strongest established predictors of romantic outcomes
emerge only from the interactions between two people or fromthe way they respond to unpredictable and uncontrollable
events that have not yet happened. Consequently, the bestestablished predictors of how a romantic relationship willdevelop can be known only after the relationship begins. The
requirement that Internet matchmaking be based solely on
qualities of individuals that can be known prior to their awareness of each other excludes these variables from considerationin matching algorithms and thus represents an upper bound to
the ability of such algorithms to predict whether a relationshipbetween two specific strangers is likely to succeed or fail.

Part II—conclusion: Online dating is superior
in some ways but not in others
Part II examined whether online dating is superior to conventional offline dating. The answer is yes and no. On the positiveside of the ledger, online dating offers access to partners forpeople who otherwise might lack it, which is a large benefit. In
addition, the ability to engage in brief computer mediatedcommunication (CMC) with potential partners has the potential to foster greater attraction upon a first meeting. Finally,
matching algorithms may be effective at omitting from thedating pool people who are likely to be poor relationship partners in general. These benefits are considerable.
On the negative side of the ledger, many aspects of online
dating do not appear to improve romantic outcomes and mighteven undermine them. For example, the widespread emphasis
on profiles as the first introduction to potential partners seems
unfortunate in light of the disconnect between what people
find attractive in a profile versus what they find attractive when

Online Dating
meeting another person face-to-face, a problem exacerbated bycomparing multiple profiles side-by-side. In addition, browsing many profiles fosters judgmental, assessment-oriented
evaluations and can cognitively overwhelm users, two processes that can ultimately undermine romantic outcomes. Furthermore, it seems that the CMC available through online
dating sites only increases attraction toward a potential partner
if the duration of CMC is brief (a few weeks or less), and it canpotentially undermine attraction if it yields unrealistic oroverly particular expectations that will be disconfirmed upona face-to-face meeting. Finally, despite grand claims to thecontrary, it is unlikely that any matching algorithm based upon
data collected before people have encountered each other canbe effective at identifying partners who are compatible for along-term relationship.

Summary and Implications
Summary of the major findings
In a remarkably short time, online dating has revolutionized
how people seek romantic partners and initiate relationships
with them. Unlike other mate-seeking innovations that neverachieved widespread appeal, such as printed personal advertisements and video-dating, online dating is enormously popular and expanding rapidly. It has fundamentally altered thedating landscape, restructuring the romantic acquaintance process and changing the nature of compatibility matching.
Overall, we view the emergence and popularity of online dating as positive developments. We concur with behavioral economist Dan Ariely’s (2010, p. 215) recent observation that thedating market for single people (the coordination mechanismthat helps them find partners efficiently) has long been “one of
the most egregious market failures in Western society,” and webelieve that harnessing the power of the Internet is a promisingmeans of improving societal levels of romantic well-being.
Although we see clear benefits in online dating for relationship seekers, especially those whose opportunities throughmore traditional dating channels are limited, we also see considerable limitations. For example, online dating sites have notbeen able to do some things likely to improve their effectiveness, such as minimizing deception or better regulating
involvement by people with a history of substance abuse or
violent crime. In addition, the incentive structure for most sites
is inconsistent with their stated aims; because dating sites lose
two paying customers (or potential advertising revenue) when
they facilitate the formation of a romantic relationship, datingsites have a perverse incentive to keep users single (although
they presumably also have an incentive for at least some of
their users to develop romantic relationships, to foster positive
word-of-mouth). Furthermore, whatever efficacy online dating possesses almost seems to occur in spite of how the particular sites implement the services of access, communication,and matching rather than because of how they do so. In oursummary of the ways online dating sites implement these services, we focus on typical procedures of prototypical sites.

49
Regarding access, it can be difficult to learn much about
potential partners from their profiles. Not only is modest misrepresentation widespread, but people seem to lack the ability
to determine from a profile which potential partners will beespecially compatible with them once they meet in person. In
addition, the process of browsing profiles side-by-side is likely
to cause users to overweight features of potential partners thatare easy to evaluate via profiles but might be largely irrelevant
once a relationship starts to develop. Side-by-side browsing is
also likely to induce an assessment mindset, causing users tocommoditize potential partners. Furthermore, many sites provide users with very large numbers of profiles, causing them to
use time-efficient but minimally thoughtful strategies forchoosing among them and potentially reducing their willingness to commit to any one partner. Moreover, many of thesesites allow users to make unlimited selections, contacting hundreds of potential partners, which can cheapen the value ofbeing contacted and overwhelm the most desirable potential
partners. Thus, although offering users a chance to consider agreat many potential partners in a simple, easy-to-use, and safe
format may provide valuable opportunities at little cost, ouranalysis suggests that current practices are likely to reduce thevalue that users receive from their participation.
Regarding communication, the interactive nature of CMC
can foster intimacy and liking between strangers, especially if
the CMC lasts for short durations (e.g., 3 weeks rather than 6).
Nevertheless, CMC falls short of the experiential richness offace-to-face interaction. The absence of face-to-face social cues
may lead communicators to fill in the gaps in often inaccurate
ways—for example, overly optimistic or overly particular viewsof the message sender, which can create expectations that are
not confirmed when people subsequently meet face-to-face. In
other words, live interaction is a complex, multifaceted processwith an essence that cannot be fully captured by CMC. In online
dating, users are probably wise to engage in CMC briefly toassess basic levels of romantic potential and then move rapidly
to face-to-face interactions. Furthermore, the closer CMC
comes to live interaction—for example, by offering more interactive channels for communication through the use of webcams
or avatars—the more likely it is to be diagnostic of face-to-facecompatibility and the more valuable users are likely to find it.
Regarding matching, online dating sites have published noresearch that is sufficiently rigorous or detailed to support theclaim that they provide more compatible matches than traditional dating does. Moreover, the major assumption underlying these claims—that long-term romantic outcomes can bepredicted substantially from individual qualities of the twopartners assessed before they have even met—is weakly supported by many years of research showing that context andinteraction have much more influence on romantic outcomesthan personality and attitudes do. It is true that, compared toother types of sites, matching sites may benefit some of theirusers by ruling out poor candidates for a relationship (e.g.,
people with a criminal history or those disinterested in a longterm relationship) and highly incompatible matches (e.g., people with very different educational backgrounds or religious

50
beliefs, who presumably also would be unlikely to start datingoffline or through self-selection sites). But this benefit, real as
it may be, should not be conflated with the claim that a proprietary algorithm can determine whether two relationship seekers are soulmates or have chemistry with each other. Instead,and until data establish otherwise, we suggest that a significant
portion of any perceived success of these sites may be attributable to (a) selection processes based upon branding (e.g.,
eHarmony might be especially successful at recruiting users
who are interested in finding a marriage partner); (b) certain
cognitive and social-psychological processes, such as the placebo effect, the endowment effect, and the confirmation bias;or (c) the inevitability that random pairings among highly
motivated, prerequisite-meeting individuals would likely yielda certain number of successful relationships.

Implications of online dating
The prevalence of online dating may be exerting unexpected
effects on romantic relationships. We speculate that online dating may affect not only the process of finding and forming
romantic relationships but perhaps also something more fundamental about the organization of family life.
Does online dating change the way people think about
and approach potential relationships? As discussed previously, online dating may encourage the adoption of an assessment mindset, in which people rapidly evaluate anotherperson’s potential as a romantic partner. This tendency is mostpervasive on sites emphasizing access, although it is also evident on sites emphasizing matching, which also offer multiple
potential partners. Assessment mindsets are most relevant to
the evaluation of profiles, but they can also compromise live
interactions.
Assessment mindsets may promote the tendency to commoditize other people. Jaron Lanier, a founder of the field of
virtual reality, made a similar point about social networking
sites, describing them as dehumanizing and fostering shallow
interactions and a “fakey-fakey social life” (Kahn, 2011, p.
46). Relationships are most satisfying when partners adopt a
communal orientation, responding mutually to each other’s
needs and preferences (Clark et al., 2010), rather than an
exchange orientation, in which benefits and costs are assessedon an ongoing basis. To the extent that online dating induces amarketplace mentality, it is likely to interfere with a communal orientation and the formation of communal bonds.
The negative impact of processes associated with an evaluative mindset may be exacerbated by the idea, promoted atmany online dating sites, that the goal of dating is to find andbond with one’s soulmate. For example, eHarmony offers“Tips to Finding Your Soulmate Online,” suggesting that evena free trial “allows you to connect to your potential soulmate
within minutes” (eHarmony, 2011c, para. 3). This soulmate
emphasis is consistent with the philosophy of eHarmony’s
founder (Warren, 2002), who at one point distributed to all

Finkel et al.
users of the site his book entitled Date . . . or Soul Mate: How
to Know if Someone Is Worth Pursuing in Two Dates or Less
(Orr, 2004, ellipses in original). Although one might expectthat such claims about finding one’s soulmate would be limited to algorithm-selection sites, they in fact pervade the online
dating landscape. For example, one self-selection site,
JDate (2011, para. 1), claims that, “Each week, hundreds of
JDaters meet their soul mates.” Another self-selection site,
PlentyOfFish.com (2011, para. 5), makes strong soulmate
claims and even suggests a rationale for why users are especially likely to find a soulmate on that site: “Do you want tofind your soulmate? Think about it, where else are you going
to find millions of singles all in one place looking for someone? If you are serious about finding a relationship you should
Signup Now and find your soulmate!!”One major problem with searching for one’s soulmate isthat the belief that a partner must be a soulmate for a romantic
relationship to succeed is associated with relationship dysfunction (Eidelson & Epstein, 1982; Epstein & Eidelson,
1981). Indeed, people with strong beliefs in romantic destiny(sometimes called soulmate beliefs)—that a relationshipbetween two people either is or is not “meant to be”—areespecially likely to exit a romantic relationship when problems arise (Franiuk, Cohen, & Pomerantz, 2002; Franiuk,
Pomerantz, & Cohen, 2004; Knee, 1998; Knee & Bush, 2008)
and to become vengeful in response to partner transgressions
when they feel insecure in the relationship (Finkel, Burnette,& Scissors, 2007). On the other hand, people with strong
beliefs in romantic growth (sometimes called work-it-out
beliefs)—that happy relationships emerge from overcoming
challenges—are especially likely to persist and succeed whenconfronting problems.
Destiny/soulmate beliefs have long been encouraged by the
media (Galician, 2004; Holmes, 2007), but the pervasivenessof online dating sites’ soulmate-related claims may well be
exacerbating this general trend. Consistent with this possibility, a January 2011 poll indicated that 73% of Americans
believe in soulmates, up from 66% six months earlier (Marist
Poll, 2011). To be sure, a destiny/soulmate mindset predicts
better outcomes when people believe that they have foundtheir soulmate and when relationships are going well (Finkel
et al., 2007; Franiuk et al., 2002; Knee, 1998; Knee & Bush,2008). However, almost all romantic relationships eventuallyencounter significant stresses and strains (for a review, see
Bradbury & Karney, 2010), which suggests that this mindset islikely to undermine relationship well-being over the long-run.
Furthermore, soulmate beliefs may encourage daters to persist in their search for the perfect mate even when they havebecome involved in potentially rewarding (albeit imperfect)
relationships. For example, in the words of Warren, eHarmony’s founder, “I encourage people to figure out the kind of
person they need to be really happy, and then to hold on to thisset of criteria to the very end. Mismatchedness will always win
out over hard work” (quoted in Orr, 2004, p. 54). In light ofsuch pronouncements, perhaps it is not surprising when online

Online Dating
daters make assertions like this one: “I would prefer to be on
my own rather than just fall into any relationship where I am
not a hundred percent happy” (Leslie & Morgan, 2009, p. 13).
Online dating may be subtly influencing daters’ approach
to potential partners in another way. By suggesting that compatibility can be established from a relatively small bank oftrait-based information about a person—whether by a matchmaker’s algorithm or by the users’ own glance at a profile—
online dating sites may be supporting an ideology ofcompatibility that decades of scientific research suggests isfalse. That is, these sites imply, and in some instances explicitly assert, that the essential qualities of a relationship can bepredicted from characteristics of the potential partners thatexist before they have met. Standing in direct contrast to thissuggestion is 75 years of scientific research, reviewed above,
indicating that pre-existing personal qualities account for avery small percentage of the variance in relationship success.
Might online dating accentuate homogamy in marriage?
Similarity is a potent principle in online dating. As described
previously, matching sites typically emphasize similarity on
certain attributes in assigning compatible matches for theirmembers. In terms of access, when people browse through theprofiles of potential dating partners, similarity also plays an
influential role. We speculate that the growing prevalence ofsuch dating sites as a resource for pair-bonding might be leading to an increase in marriages based on similarity within our
culture.
Homogamy has long been recognized as a selection factor
in marriage, although scholars have argued that the observed
similarities between spouses might be an artifact of three tendencies: (a) that people’s everyday activities reflect theirsociodemographic characteristics and attitudinal preferences; (b) that people therefore tend to come into contact
with others whose sociodemographic characteristics and attitudinal preferences are like their own; and (c) that people
tend to marry people with whom they have come into contact(Kerckhoff, 1964; Schellenberg, 1960; Winch, 1958). This
field-of-eligibles hypothesis has long been one explanation
for spousal correlations, in contrast to the possibility that
there is a preference for similar others (Berscheid & Reis,
1998). Indeed, homogamous assortative mating—the tendency of sexually reproducing organisms to choose to matewith similar others—can occur in a population even when
there is no individual preference for it (Burley, 1983).
Online dating redefines the field of eligibles. Sites thatoffer matching services generally rely on similarity algorithms(with some amount of complementarity sprinkled in), matching potential partners on the basis of numerous personality
traits and values, some of which are unlikely to play much ofa role in determining more traditional fields of eligibles. It is
instructive to imagine a plausible hypothetical case in which aparticular dating site matches people on the basis of similarity
on the personality traits of self-esteem and conscientiousness.
In everyday life, people likely come into contact with people
whose self-esteem and conscientiousness are distributed

51
across the full spectrum of this dimension. Thus, even if potential daters ignore self-esteem and conscientiousness in making
their choices from the set of candidates provided by a matching site, we would expect a greater degree of homogamy in thepool of couples so formed, compared to couples formed from
more traditional encounters. This occurs because, as a result of
the matching algorithm, the field of eligibles is more homogenous with regard to self-esteem and conscientiousness in theformer than in the latter case. In other words, and somewhat
ironically, although users almost universally think of online
dating as expanding their field of eligibles, it may actually beshrinking crucial aspects of the diversity of that field.
The consequences of this move toward homogamy may beconsiderable. For example, self-expansion theory suggeststhat people might actually prefer to associate with dissimilar
others if they feel confident in the other’s acceptance of them(Aron & Aron, 1997; Aron, Steele, Kashdan, & Perez, 2006).
The reduced opportunity to date dissimilar others likely to befostered by matching sites, however, seems unlikely to provide the sort of experiences that would promote feeling comfortable with dissimilar others. In the words of Coren (2006),
I can’t help suspecting that ‘similarity’ works best as aseries of serendipitous realisations. You start by thinking
that somebody looks quite nice, and seems quite nice. So
you meet them again. And gradually, over time, you happily discover that they too enjoy Philip Roth novels, orsafari parks, or latex handcuffs, and it feels like a sort ofmagic. But if you begin by demanding similarity, if you
advertise to have your fetishes met, then surely all you
can ever discover, over time, is difference? If you meetup with somebody because you know in advance thatthey share your love of Philip Roth, you’ll end up disappointed that they prefer Sabbath’s Theater to American
Pastoral. . . . Your surprise is not the magical chime, but
the jarring bell of discord.
Moreover, dating partners commonly become acquainted
with, and often develop close ties to, each other’s social networks (Sprecher, Felmlee, Orbuch, & Willetts, 2002). Contact with dissimilar others through social networks can help
broaden one’s perspective. For example, having a closefriendship with a person who is close to a member of a different ethnic or racial group is associated with more positive
intergroup attitudes and reduced prejudice (Wright, Aron,
McLaughlin-Volpe, & Ropp, 1997). Similarly, partners often
help each other develop new skills and perspectives by exposing each other to activities and viewpoints not previously
attempted. To the extent that the pool of eligibles provided by
an Internet matching site is less diverse (ethnically, attitudinally, or in any other respect relevant to its algorithm) thanthe pool of eligibles encountered in ordinary activity, then wewould expect less self-expansion of this sort to occur.
One might even speculate about potential effects on thehuman gene pool if the number of couples having their origin
through matching sites were to continue to increase. Biometric

52
genetic models counterintuitively indicate that assortative
mating for a heritable trait will actually increase phenotypicvariability, by increasing additive genetic variance, as first
demonstrated by Sewall Wright in 1921 and elaborated byJinks and Fulker (1970).9 All human behavioral traits are atleast somewhat heritable (Turkheimer, 2000)—a recent review
of several meta-analyses reported a global heritability estimate(h2) of 41% of phenotypic variance (Malouff, Rooke, &Schutte, 2008), and a qualitative synthesis by Pinker (2002)
put this value at around 50%. Therefore, it follows that if theextent of assortative mating is greater for couples createdthrough matching sites than for traditional couples, then thephenotypic variability of human traits, as well as the genetically determined component of those traits, will increase.

Implications of the present
analysis for public policy
There are at least two broad areas in which public policy is
relevant to online dating: (a) protecting the safety and privacyof users and (b) substantiating the advertised claims of online
dating sites. Concerning safety and privacy, by and large, there
has been little scrutiny of online dating sites by governmental
regulatory authorities. No federal regulatory authority currently exists for online dating; in fact, as Bramble (2011)
describes, the International Marriage Broker Act, passed in
2005, which some have suggested might be applied to online
dating, pertains only to entities that offer foreign marriage
partners to U.S. citizens (Internet Business Law Services,
2011). A few states have passed safety-oriented laws. For
example, the first such law, New Jersey’s Internet Safety Dating Act, enacted in 2008, begins with the premise that “there isa public safety need to disclose whether criminal history background screenings have been performed and to increase publicawareness of the possible risks associated with online dating
activities” (State.nj.us, 2011, para. 2a). This law requires
online dating sites that charge membership fees and that operate in New Jersey to state clearly whether or not they conduct
background checks and to educate members about safe dating
practices (Bramble, 2011). A roughly similar New York law,enacted in 2009, “requires online dating services to provide
safety awareness notification that includes, at minimum, a list
and description of safety measures reasonably designed toincrease awareness of safer dating practices” (open.nysenate.
gov, 2011, para 1).
As for the second area, substantiating advertised claims,
our impression is that policymakers and regulatory authorities
have neglected this concern. In our view, regulatory authorities ought to subject the claims of online dating sites to thesame degree of scrutiny as is applied to other advertised claims
that are relevant to public well-being. As discussed previously,federal agencies such as the FTC and FDA are charged withensuring that the advertised claims for a given product or service are substantiated, especially when those claims are

Finkel et al.
ascribed to the results of testing and “especially when [theclaims] concern health, safety, or performance” (Bureau ofConsumer Protection, 2011a, para. 4). That the consequencesof online dating concern health and safety is uncontroversial;perhaps more than any other domain of human activity, relationships affect the emotional well-being, physical health, andproductivity of partners, their children and families, and society as a whole. The need for regulatory scrutiny is particularly
clear for matching sites, several of which assert that their algorithms are supported by scientific research. Even something as
simple as disclaimers for consumer testimonials—that is,accompanying a glowing personal endorsement with the statement that such results are not typical, an official FTC guideline (Federal Trade Commission, 2011)—are rare, perhaps
even nonexistent, in the marketing appeals of online datingsites. We are aware of no such scrutiny by federal or state regulatory agencies, despite dating sites’ extensive and unsubstantiated claims.

Implications of the present analysis for
relationship-seekers
The present review has identified a series of problems that canarise when people use online dating sites. Some of these problems might be ameliorated by informing consumers how totake advantage of the opportunities that online dating affords
without succumbing to its pitfalls.
The most promising feature of online dating is probably theaccess it provides to large numbers of potential dating partners. Having choice is often a good thing, especially whenaccess to dating partners through traditional means is limited.
At the same time, too much choice can lead people to use ineffective screening strategies. We suggest that users limit themselves to a relatively manageable set of candidates within a
specific time period and avoid an overemphasis on contacting
those users who are most consensually desirable “on paper.”They should try to avoid an assessment mindset and instead
try to imagine what they might do to have an enjoyable interaction with a person. They should be open-minded to considering potential partners who may not fulfill all of their desired
criteria, although they should avoid developing overly lofty or
particular expectations regarding partners before meeting
face-to-face. They should prioritize distinctive criteria in
potential partners rather than criteria most other users are prioritizing. They should craft their profiles carefully, highlight
qualities about themselves that differ from the standard profile
fare, and log in to the dating site frequently.
The more channels for communication that a dating site
provides, the more diagnostic users’ early impressions are likely
to be. At the same time, users should avoid overdoing online
communications—the sooner two people meet face-to-face,
the more likely they are to have benefited from the preceding
CMC. Users may also want to view online communication as
an opportunity to try interacting with potential partners who

Online Dating
may be outside of their comfort zone—that is, partners withwhom one might not otherwise come into contact.
In light of the available data and the fact that many of themost important predictors of relationship success are simply
unavailable prior to a relationship’s beginning, we are not
optimistic that mathematical matching algorithms can be especially effective at finding compatible partners for users. We
encourage users to consider this limitation before investing thesometimes-considerable resources required to join certain
matching sites. Matching algorithms may be effective at eliminating from the dating pool potential partners who are likely tohave poor romantic outcomes in most of their romantic relationships. Eliminating such partners is potentially a valuable
service, as long as the user in question does not fall into theeliminated category.

Conclusion
Romantic relationships can begin anywhere. When Cupid’s
arrow strikes, you might be at church or at school, playing chessor softball, searching for a partner at a party, or minding yourown business on the train. But sometimes Cupid goes on vacation, or takes a long nap, or kicks back for a marathon of Lifetime original movies. As a result, people go through stretches oftime when desirable potential partners seem out of reach. Insteadof waiting for Cupid to get back to work, people sometimes join
dating sites to take back some control of their romantic lives.
Indeed, online dating has enormous potential to ameliorate
what is for many people a time-consuming and often frustrating activity: the pursuit of a committed, emotionally satisfying
romantic relationship. For millennia, cultures have createddiverse practices to fulfill the evolutionary imperatives of mating and reproduction (Coontz, 2005). In the modern Westernworld, people are largely expected to identify romantic partners on their own initiative, a process that typically entails significant effort, time, and ambiguity, as well as many, often
painful, missteps. Thus, it comes as no surprise that people
would be attracted to a new medium that offers to improve theefficiency and effectiveness of this process. We share thisattraction and see much to like in the prospect of online dating.
All other things being equal, having access to many potential
partners is better than having access to few or none. Being able
to communicate with potential partners safely and conveniently offers an attractive precursor to face-to-face encounters with complete strangers. Confidence that particularly poor
relationship partners have been weeded out of the dating poolis an appealing prospect. In short, the potential of online dating to improve the process of finding and securing a satisfying,committed romantic relationship is great.
At the same time, we see substantial opportunities for
improving the way online dating is practiced. Some of thisimprovement can come from closer collaboration between
scholars and service providers. Relationship scientists havemuch to offer those who operate on the frontlines of romantic
relationship formation. Complementarily, online dating offers

53
an unprecedented opportunity for researchers to test their theories and develop new ones with large samples of participants
who are highly motivated to establish romantic relationships.
This sort of collaboration has produced important advances
and applications in many other disciplines, and the time is ripe
for a similar collaboration in this arena.
Another key source of improvement can come fromadvances in Internet technology. Mostly, the models currently
in use represent relatively early stages in Internet methods. We
envision many other possibilities, some of which are alreadyavailable. For example, knowledge networks might be createdon the basis of postdate ratings, much as Netflix currently doeswith films. This approach would encourage honesty (because
dishonest self-descriptions would quickly be reported by otherusers) and provide more information about potential partners.
Other possibilities involve enhancing opportunities for interaction through the use of video, avatars, virtual environments,and social networks. The closer the approximation to live
interaction, the more useful CMC-based impressions will be.
In the final analysis, is online dating unique from, and doesit yield superior romantic outcomes to, conventional offline
dating? The answer to the uniqueness question is an unqualified yes: Online dating is pervasive, and it has fundamentally
altered both the romantic acquaintance process and the process of compatibility matching. The answer to the superiority
question is more qualified. Online dating offers access topotential partners whom people would be unlikely to meetthrough other avenues, and this access yields new romantic
possibilities. On the other hand, the heavy emphasis on profilebrowsing at most dating sites has considerable downsides, andthere is little reason to believe that current compatibility algorithms are especially effective. Online dating functions best to
the degree that it introduces people to potential partners theywould have been unlikely to encounter otherwise and facilitates a rapid transition to face-to-face interaction, where thetwo people can get a clearer sense of their romantic potential.
As online dating evolves and matures, it seems likely that
more and more of us will first encounter romantic partners
online. Cupid’s aim will be most precise to the extent thatonline dating sites are informed by rigorous psychologicalscience.
Acknowledgment
Aside from the first author, authorship order is alphabetical; theseauthors contributed equally. The authors thank the Association for
Psychological Science for its support in the preparation of this article
and Candida Abrahamson, George Levinger, Jim McNulty, and Lisa
Neff for their insightful feedback on a previous draft.

Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.

Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.

54
Notes
1. For example, during the final revisions of this article in late 2011,
PlentyOfFish officially changed its name to POF. We did not alter the
text to reflect this 11th-hour change.
2. “Online dating” is a misnomer because the dating that is facilitated by these sites does not typically take place online. Rather, users
make initial contact online toward the goal of forming a face-to-facerelationship. A more apt term for “dating sites” might be “Internet
relationship initiation sites,” and a more apt term for “online dating”
might be “the Internet relationship initiation process,” but we adopted
the more felicitous, familiar terms for ease of presentation.
3. As noted previously and in Table 1, examining how romantic relationships can emerge via general social networking sites like Facebook is beyond the scope of this article. Such initiation frequently
involves turning acquaintances or friends-of-friends into lovers,
which means that it involves different sorts of issues from the ones
that initiation through online dating sites involves.
4. It is possible that certain processes could promote short-term successbut undermine long-term success, or vice versa. However, our review ofthe empirical literature suggests that such disconnects are rare. As such,
we do not address them in this article. In no case do we discuss an effectrelevant to initial attraction that has been demonstrated to reverse inpredicting long-term relationship well-being or vice versa.
5. Technically, mutual mediated communication involves two substeps—communicating through the dating site and communicating
without mediation by the dating site—but the processes are similar,
so we discuss them as a single step. The communication in the second
substep is still mediated in the sense it is not face-to-face; it generallytranspires via e-mail, texts, or phone.
6. Gays and Lesbians are also rejected from the site, although after asettlement in response to a discrimination complaint, eHarmony created a new site, CompatibleMatches, for them.
7. One apparent exception is an article by Carter and Buckwalter(2009), both employees of eHarmony, who reported findings suggestingthat eHarmony couples are more similar and more satisfied than couples
who met without the help of a matchmaking service. This article waspublished in Interpersona, an online, open access, peer-reviewed journal published by the Brazilian Association for Interpersonal Relationship Research. It provides no information about how eHarmony couples
were formed, stating that its algorithms “must remain proprietary” (p.
119). As noted earlier, this lack of information makes it impossible forindependent researchers to try to replicate the findings or to evaluate
possible alternative explanations to the authors’ desired conclusions,
thereby undermining essential tenets of good science.
8. Joint versus separate evaluation modes exist as endpoints of a continuum rather than as a strict dichotomy; for ease of presentation, we
adopt the convention of discussing them as a dichotomy.
9. In a single generation, positive assortative mating increases phenotypic variance on a given trait by a factor of .5rh4 of the phenotypicvariance in a population that mates randomly, where r is the correlation between the parents’ phenotypic values on that trait and h4 is
heritability (h2) squared (Falconer, 1981; also see Hohenboken, 1985).

References
Adams, C. R. (1946). The prediction of adjustment in marriage. Educational and Psychological Measurement, 6, 185–193.
Adelman, M. B., & Ahuvia, A. C. (1991). Mediated channels for mateseeking: A solution to involuntary singlehood? Critical Studies in
Mass Communication, 8, 273–289.

Finkel et al.
Ahuvia, A. C., & Adelman, M. B. (1992). Formal intermediaries in
the marriage market: A typology and review. Journal of Marriage
and the Family, 54, 452–463.
Ahuvia, A. C., Adelman, M. B., & Izberk-Bilgin, E. (2009). Commercial channels for mate seeking. In H. Reis & S. Sprecher (Eds.),
Encyclopedia of human relationships (Vol. 1, pp. 45–48). Newbury Park, CA: Sage.
Altman, I., & Taylor, D. A. (1973). Social penetration: The development of interpersonal relationships. New York, NY: Holt, Rinehart and Winston.
Amato, P. R., & Booth, A. (2001). The legacy of parents’ marital
discord: Consequences for children’s marital quality. Journal of
Personality and Social Psychology, 81, 627–638.
Amato, P. R., & DeBoer, D. D. (2001). The transmission of marital
instability across generations: Relationship skills or commitment
to marriage? Journal of Marriage and the Family, 63, 1038–1051.
Ambady, N., Bernieri, F. J., & Richeson, J. A. (2000). Toward a
histology of social behavior: Judgmental accuracy from thinslices of the behavioral stream. In M. P. Zanna (Ed.), Advancesin experimental social psychology (Vol. 32, pp. 201–271). San
Diego, CA: Academic Press.
American Psychological Association. (2001). Publication manual of
the American psychological association (5th ed.). Washington,
DC: Author.
Anderson, T. L. (2005). Relationships among Internet attitudes, internet use, romantic beliefs, and perceptions of online romantic relationships. CyberPsychology & Behavior, 8, 521–531.
Antheunis, M. L., Valkenburg, P. M., & Peter, J. (2007). Computermediated communication and interpersonal attraction: An experimental test of two explanatory hypotheses. CyberPsychology &Behavior, 10, 831–836.
Antill, J. K. (1983). Sex role complementarity versus similarity inmarried couples. Journal of Personality and Social Psychology,
45, 145–155.
Aretz, W., Demuth, I., Schmidt, K., & Vierlein, J. (2010). Partner
search in the digital age. Psychological characteristics of onlinedating-service-users and its contribution to the explanation ofdifferent patterns of utilization. Journal of Business and Media
Psychology, 1, 8–16. Available from www.journal-bmp.de/
Argyle, M., & Dean, J. (1965). Eye-contact, distance and affiliation.
Sociometry, 28, 289–304.
Ariely, D. (2010). The upside of irrationality. New York, NY: Harper
Collins.
Armor, D. A., & Taylor, S. E. (2003). The effects of mindset on
behavior: Self-regulation in deliberative and implemental framesof mind. Personality and Social Psychology Bulletin, 29, 86–95.
Aron, A., & Aron, E. N. (1997). Self-expansion motivation andincluding other in the self. In S. Duck (Ed.), Handbook of
personal relationships: Theory, research, and interventions (2nded., pp. 251–270). Chichester, UK: John Wiley.
Aron, A., Steele, J., Kashdan, T. B., & Perez, M. (2006). When similars don’t attract: Tests of a prediction from the self-expansion
model. Personal Relationships, 13, 387–396.
Aronson, E., & Linder, D. (1965). Gain and loss of esteem as determinants of interpersonal attractiveness. Journal of Experimental
Social Psychology, 1, 156–172.

Online Dating
Asch, S. E. (1946). Forming impressions of personality. Journal of
Abnormal and Social Psychology, 41, 258–290.
Aube, J., & Koestner, R. (1995). Gender characteristics and relationship adjustment: Another look at similarity complementarity
hypotheses. Journal of Personality, 63, 879–904.
Badoo.com. (2011, December 19). Badoo corporate. Retrieved fromhttp://corp.badoo.com/
Baker, A. (2008). Down the rabbit hole: The role of place in the initiation and development of online relationships. In A. Barak (Ed.),
Psychological aspects of cyberspace: Theory, research, applications (pp. 163–184). Cambridge, UK: Cambridge University Press.
Baldwin, M. R. (1992). Relational schemas and the processing ofsocial information. Psychological Bulletin, 112, 461–484.
Bargh, J. A., McKenna, K. Y. A., & Fitzsimons, G. M. (2002). Can
you see the real me? Activation and expression of the “true self”
on the Internet. Journal of Social Issues, 58, 33–48.
Barraket, J., & Henry-Waring, M. S. (2008). Getting it on(line):
Sociological perspectives on e-dating. Journal of Sociology, 4,
149–165.
Bateson, P. P. G. (1983). Optimal outbreeding. In P. P. G. Bateson(Ed.), Mate choice (pp. 257–277). Cambridge, UK: Cambridge
University Press.
Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire
for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529.
Baym, N. K. (2006). Interpersonal life online. In L. A. Lievrouw &S. Livingstone (Eds.), Handbook of new media: Social shaping
and social consequences of ICTs (2nd ed., pp. 35–54). London,
England: SAGE.
Beach, S. R. H., Whitaker, D. J., Jones, D. J., & Tesser, A. (2001).
When does performance feedback prompt complementarity inromantic relationships? Personal Relationships, 18, 231–248.
Ben-Ze’ev, A. (2004). Love online: Emotions on the Internet. Cambridge, UK: Cambridge University Press.
Berscheid, E., Graziano, W., Monson, T., & Dermer, M. (1976). Outcome dependency: Attention, attribution, and attraction. Journalof Personality and Social Psychology, 34, 978–989.
Berscheid, E., & Reis, H. T. (1998). Attraction and close relationships. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The
handbook of social psychology (4th ed., Vol. 2, pp. 193–281).
New York, NY: McGraw-Hill.
Blossfeld, H. P. (2009). Educational assortative marriage in comparative perspective. Annual Review of Sociology, 35, 513–530.
Botti, S., & Hsee, C. K. (2010). Dazed and confused by choice:
How the temporal costs of choice freedom lead to undesirable
outcomes. Organizational Behavior and Human Decision Processes, 112, 161–171.
Botti, S., & Iyengar, S. S. (2004). The psychological pleasure andpain of choosing: When people prefer choosing at the cost of subsequent satisfaction. Journal of Personality and Social Psychology, 87, 312–326.
Botti, S., Orfali, K., & Iyengar, S. S. (2009). Tragic choices: Autonomy and emotional responses to medical decisions. Journal ofConsumer Research, 36, 337–352.

55
Bouchard, G., Lussier, Y., & Sabourin, S. (1999). Personality andmarital adjustment: Utility of the five-factor model of personality. Journal of Marriage and the Family, 61, 651–660.
Bowlby, J. (1969). Attachment and loss: Volume I. Attachment. New
York, NY: Basic Books.
Bowlby, J. (1973). Attachment and loss: Volume II. Separation: Anxiety and anger. New York, NY: Basic Books.
Bowlby, J. (1980). Attachment and loss: Volume III. Loss: Sadness
and depression. New York, NY: Basic Books.
Bown, N. F., Read, D., & Summers, B. (2003). The lure of choice.
Journal of Behavioral Decision Making, 16, 297–308.
Bradbury, T. N., & Karney, B. R. (2010). Intimate relationships. New
York, NY: W. W. Norton.
Bramble, E. (2011). Communication law and online dating. Unpublished manuscript, Columbus School of Law, The Catholic University of America, Washington, DC.
Bramlett, M. D., & Mosher, W. D. (2002). Cohabitation, marriage,
divorce, and remarriage in the United States (Vital and Health
Statistics No. Series 23, Number 22). Hyattsville, MD: National
Center for Health Statistics.
Brooks, M. (2006, June 9). Interviewing Plenty of Fish CEO, Markus
Frind. Retrieved from http://www.onlinepersonalswatch.com/
news/2006/06/opw_interview_p.html
Brym, R. J., & Lenton, R. L. (2003). Love at first byte: Internet dating in Canada. Unpublished Manuscript, University of Toronto,
Ontario, Canada.
Buckwalter, J. G., Carter, S. R., Forgatch, G. T., Parsons, T. D., &Warren, N. C. (2004). Method and system for identifying people who are likely to have a successful relationship (U.S. Patent
6,735,568). Washington, DC: U.S. Patent and Trademark Office
Buckwalter, J. G., Carter, S. R., Forgatch, G. T., Parsons, T. D., &Warren, N. C. (2008). Method and system for identifying people
who are likely to have a successful relationship (U.S. Patent 7,454,357B2). Washington, DC: U.S. Patent and Trademark Office
Buehlman, K. T., Gottman, J. M., & Katz, L. Y. (1992). How a couple
views their past predicts their future: Predicting divorce from anoral history interview. Journal of Family Psychology, 5, 295–318.
Bureau of Consumer Protection. (2011a, June 14). Advertisingand marketing on the Internet: Rules of the road. Retrieved
from http://business.ftc.gov/documents/bus28-advertising-andmarketing-internet-rules-road
Bureau of Consumer Protection. (2011b, August 1). Substantiation:
The science of compliance. Retrieved from http://business.ftc.gov/documents/substantiation-science-compliance
Burgess, E. W., & Cottrell, L. S. (1939). Predicting success or failure
in marriage. New York, NY: Prentice Hall.
Burley, N. (1983). The meaning of assortative mating. Ethology andSociobiology, 4, 191–203.
Byrne, D. (1961). Interpersonal attraction and attitude similarity.
Journal of Abnormal and Social Psychology, 62, 713–715.
Byrne, D., Ervin, C. R., & Lamberth, J. (1970). Continuity between
the experimental study of attraction and real-life computer
dating. Journal of Personality and Social Psychology, 16,
175–165.

56
Cacioppo, J. T., Hawkley, L. C., Crawford, L. E., Ernst, J. M.,
Burleson, M. H., Kowalewski, R. B., . . . Berntson, G. G. (2002).
Loneliness and health: Potential mechanisms. Psychosomatic
Medicine, 64, 407.
Campbell, D., & Stanley, J. (1963). Experimental and quasiexperimental designs for research. Chicago, IL: Rand-McNally.
Carter, S. R., & Buckwalter, J. G. (2009). Enhancing mate selection through the Internet: A comparison of relationship quality
between marriages arising from an online matchmaking system
and marriages arising from unfettered selection. Interpersona:
An International Journal on Personal Relationships, 3, 105–125.
Casewatch. (2011, May 24). Retrieved from http://www.casewatch.org/civil/enviga/complaint.shtml
Caspi, A., & Gorsky, P. (2006). Online deception: Prevalence, motivation, and emotion. CyberPsychology & Behavior, 9, 54–59.
Charles, K. K., & Stephens, M. (2004). Disability, job displacementand divorce. Journal of Labor Economics, 22, 489–522.
Chemistry.com. (2011, July 19). Why choose chemistry? Retrieved
from http://www.chemistry.com/cp/whychemistry/
Cherlin, A. J., Burton, L. M., Hurt, T. R., & Purvin, D. M. (2004). The
influence of physical and sexual abuse on marriage and cohabitation. American Sociological Review, 69, 768–789.
Chernev, A. (2006). Decision focus and consumer choice amongassortments. Journal of Consumer Research, 33, 50–59.
Chernev, A., Bockenholt, U., & Goodman, J. (2010). Commentary on
Scheibehenne, Greifeneder, and Todd-Choice overload: Is there
anything to it? Journal of Consumer Research, 37, 426–428.
Chiou, W., & Yang, M. (2010). The moderating role of need for cognition on excessive searching bias: A case of finding romantic
partners online. In B. K. Wiederhold, G. Riva, & S. I. Kim (Eds.),
Annual review of cybertherapy and telemedicine (pp. 120–122).
Amsterdam, Netherlands: IOS Press BV.
Christakis, N. A., & Fowler, J. H. (2009). Connected: The surprising
power of our social networks and how they shape our lives. New
York, NY: Little Brown.
Clark, M. S. (1984). Record keeping in two types of relationships.
Journal of Personality and Social Psychology, 47, 549–557.
Clark, M. S., Lemay, E. P., Graham, S. M., Pataki, S. P., & Finkel,
E. J. (2010). Ways of giving and receiving benefits in marriage:
Norm use and attachment related variability. Psychological Science, 21, 944–951.
Cocks, H. G. (2009). Classified: The history of the personal column.
London, England: Random House.
Cohan, C. L., & Cole, S. W. (2002). Life course transitions and natural disaster: Marriage, birth, and divorce following Hurricane
Hugo. Journal of Family Psychology, 16, 14–25.
Cohen, S., Frank, E., Doyle, W. J., Skoner, D. P., Rabin, B. S., &Gwaltney, J. M., Jr. (1998). Types of stressors that increase susceptibility to the common cold in healthy adults. Health Psychology, 17, 214–223.
Conger, R. D., & Conger, K. J. (2008). Understanding the processes
through which economic hardship influences families and children. In D. R. Crane & T. B. Heaton (Eds.), Handbook of families
and poverty (pp. 64–81). Thousand Oaks, CA: SAGE.

Finkel et al.
Conger, R. D., Rueter, M. A., & Elder, G. H. (1999). Couple resilience to economic pressure. Journal of Personality and Social
Psychology, 76, 54–71.
Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation:
Design and analysis issues for field settings. Chicago, IL: RandMcNally.
Coontz, S. (2005). Marriage, a history. New York, NY: Viking.
Cooper, A., & Sportolari, L. (1997). Romance in cyberspace: Understanding online attraction. Journal of Sex Education and Therapy,
22, 7–14.
Coren, V. (2006, October 15). The curse of true love. The Guardian.
Retrieved from http://www.guardian.co.uk/commentisfree/2006/
oct/15/comment.victoriacoren
Cornwell, B., & Lundgren, D. C. (2001). Love on the Internet:
Involvement and misrepresentation in romantic relationships incyberspace vs. realspace. Computers in Human Behavior, 17,
197–211.
Culnan, M. J., & Markus, M. L. (1987). Information technologies. In
F. M. Jablin, L. L. Putnam, K. H. Roberts, & L. W. Porter (Eds.),
Handbook of organizational communication: An interdisciplinary perspective (pp. 420–443). Newbury Park, CA: SAGE.
Cuperman, R., & Ickes, W. (2009). Big Five predictors of behavior
and perceptions in initial dyadic interactions: Personality similarity helps extraverts and introverts, but hurts “disagreeables.”Journal of Personality and Social Psychology, 97, 667–684.
Daft, R. L., & Lengel, R. H. (1984). Information richness: A new
approach to managerial behavior and organizational design. In B. M.
Staw & L. L. Cummings (Eds.), Research in organizational
behavior (Vol. 6, pp. 191–233). Greenwich, CT: JAI.
Darden, D., & Koski, P. R. (1988). Using the personals ads: A deviant
activity? Deviant Behavior, 9, 383–400.
Davila, J., Karney, B. R., Hall, T. W., & Bradbury, T. N. (2003).
Depressive symptoms and marital satisfaction: Within-subject
associations and the moderating effects of gender and neuroticism. Journal of Family Psychology, 17, 557–570.
Day, M., Hamilton, A., Hutchins, J., Maher, J., & Vance, N. (2010).
Making the leap: Tracking the online dating process from computer mediated communication to face-to-face interaction.
Unpublished Manuscript, Pratt Institute, Brooklyn, New York.
Deci, E. L., & Ryan, R. M. (1991). A motivational approach to theself: Integration in personality. In R. Dienstbier (Ed.), Nebraska
symposium on motivation: Vol. 38. Perspectives on motivation(pp. 237–288). Lincoln: University of Nebraska Press.
Decuyper, M., De Bolle, M., & De Fruyt, F. (in press). Personality
similarity, perceptual accuracy, and relationship satisfaction in
dating and married couples. Personal Relationships.
Derks, D., Bos, A. E. R., & von Grumbkow, J. (2007). Emoticons
and social interaction on the Internet: The importance of social
context. Computers in Human Behavior, 23, 842–849.
Diener, E., & Seligman, M. E. P. (2002). Very happy people. Psychological Science, 13, 81–84.
Doan, T. T., & Meyers, S. A. (2011, May). Online dating: Determining the presence of a stigma. Poster session presented at the meeting of Midwest Psychological Association, Chicago, IL.

Online Dating
Donn, J. E., & Sherman, R. C. (2002). Attitudes and practices regarding the formation of romantic relationships on the Internet.
CyberPsychology & Behavior, 5, 107–123.
D’Onofrio, B. M., Eaves, L. J., Murrelle, L., Maes, H. H., & Spilka,
B. (1999). Understanding biological and social influences on
religious affiliation, attitudes, and behaviors: A behavior genetic
perspective. Journal of Personality, 67, 953–984.
Drigotas, S. M., & Rusbult, C. E. (1992). Should I stay or should I
go? A dependence model of breakups. Journal of Personality andSocial Psychology, 62, 62–87.
Dryer, D. C., & Horowitz, L. M. (1997). When do opposites attract?
Interpersonal complementarity versus similarity. Journal of Personality and Social Psychology, 72, 592–603.
Dunn, E. W., Wilson, T. D., & Gilbert, D. T. (2003). Location,
location, location: The misprediction of satisfaction in housing lotteries. Personality and Social Psychology Bulletin, 29,1421–1432.
Dyrenforth, P. S., Kashy, D. A., Donnellan, M. B., & Lucas, R. E.
(2010). Predicting relationship and life satisfaction from personality in nationally representative samples from three countries:
The relative importance of actor, partner, and similarity effects.
Journal of Personality and Social Psychology, 99, 690–702.
Eastwick, P. W., Eagly, A. H., Finkel, E. J., & Johnson, S. E. (2011).
Implicit and explicit preferences for physical attractiveness ina romantic partner: A double dissociation in predictive validity.
Journal of Personality and Social Psychology, 101, 993–1011.
Eastwick, P. W., & Finkel, E. J. (2008a). Sex difference in mate preferences revisited: Do people know what they initially desire in aromantic partner? Journal of Personality and Social Psychology,
94, 245–264.
Eastwick, P. W., & Finkel, E. J. (2008b). Speed-dating: A powerfuland flexible paradigm for studying romantic relationship initiation. In S. Sprecher, A. Wenzel, & J. Harvey (Eds.), Handbook of
relationship initiation (pp. 217–234). New York, NY: Guilford.
Eastwick, P. W., Finkel, E. J., & Eagly, A. H. (2011). When and why
do ideal partner preferences affect the process of initiating andmaintaining romantic relationships? Journal of Personality andSocial Psychology, 101, 1012–1032.
Eastwick, P. W., Finkel, E. J., Mochon, D., & Ariely, D. (2007).
Selective versus unselective romantic desire: Not all reciprocity
is created equal. Psychological Science, 18, 317–319.
The Economist. (2010, December 29). Love at first byte. Retrieved
from http://www.economist.com/node/17797424.
eHarmony.com. (2011a, July 15). Retrieved from http://www.eharmony.com/
eHarmony.com. (2011b, July 15). How we match. Retrieved fromhttp://www.eharmony.com/why
eHarmony.com. (2011c, July 15). Tips to finding your soulmate online. Retrieved from http://eharmonyfreetrial.com/2011/01/tipsto-finding-your-soulmate-online/
eHarmony Scientific Match Making. (2011). Retrieved from http://
www.eharmony.com/why/science
Eidelson, R. J., & Epstein, N. (1982). Cognition and relationshipmaladjustment: Development of a measure of dysfunctional
relationship beliefs. Journal of Consulting and Clinical Psychology, 50, 15–20.

57
Ellison, N., Heino, R., & Gibbs, J. (2006). Managing impressions online:
Self presentation processes in the online dating environment. Journal of Computer Mediated Communication, 11, 415–441.
Epstein, N., & Eidelson, R. J. (1981). Unrealistic beliefs of clinicalcouples: Their relationship to expectations, goals and satisfaction. American Journal of Family Therapy, 9, 13–22.
Falconer, D. S. (1981). Introduction to quantitative genetics (2nded.). London, England: Longman.
Federal Trade Commission. (2011). FTC publishes final guides governing endorsements, testimonials. Retrieved from http://www.ftc.gov/opa/2009/10/endortest.shtm
Feng, D., Giarrusso, R., Bengtson, V. L., & Frye, N. (1999). Intergenerational transmission of marital quality and marital instability.
Journal of Marriage and the Family, 61, 451–463.
Festinger, L., Schachter, S., & Back, K. (1950). Social pressures in
informal groups. New York, NY: Harper.
Finkel, E. J., & Baumeister, R. F. (2010). Attraction and rejection.
In R. F. Baumeister & E. J. Finkel (Eds.), Advanced social psychology: The state of the science (pp. 419–459). New York, NY:
Oxford University Press.
Finkel, E. J., Burnette, J. L., & Scissors, L. E. (2007). Vengefully
ever after: Destiny beliefs, state attachment anxiety, and forgiveness. Journal of Personality and Social Psychology, 92,
871–886.
Finkel, E. J., & Eastwick, P. W. (2008). Speed-dating. Current Directions in Psychological Science, 17, 193–197.
Finkel, E. J., & Eastwick, P. W. (in press). Interpersonal attraction. In
J. A. Simpson & J. F. Dovidio (Eds.), Handbook of personality
and social psychology: Interpersonal relations and group processes. Washington, DC: American Psychological Association.
Finkel, E. J., Eastwick, P. W., & Matthews, J. (2007). Speed-dating
as an invaluable tool for studying romantic attraction: A methodological primer. Personal Relationships, 14, 149–166.
Fiore, A. T., & Donath, J. S. (2004). Online personals: An overview. Paper session presented at the meeting of ACM ComputerHuman Interaction, Vienna, Austria.
Fiore, A. T., Taylor, L. S., Mendelsohn, G. A., & Hearst, M. (2008).
Assessing attractiveness in online dating profiles. Proceedingsof Computer-Human Interaction (pp. 797–806). New York, NY:
ACM Press.
Fiore, A. T., Taylor, L. S., Zhong, X., Mendelsohn, G. A., & Cheshire,
C. (2010). Who’s right and who writes: People, profiles, contacts,
and replies in online dating. Retrieved from http://www.computer.org/csdl/proceedings/hicss/2010/3869/00/index.html.
Fisher, H. (2008, June 8). Q/A with Helen Fisher. Retrieved fromhttp://www.facebook.com/note.php?note_id=40673448583
Fisher, H. (2009). Why him? Why her? New York, NY: Henry Holt.
Fisher, H. (2011, February 14). The chemistry of attraction: Understanding the science of your love personality to attract your soulmate. Retrieved from http://www.soulmatesummit.net/upgrade/
Fisher, H. E., Rich, J., Island, H. D., & Marchalik, D. (2010). The
second to fourth digit ratio: A measure of two hormonally-based
temperament dimensions. Personality and Individual Differences, 49, 773–777.
Fisher, H. E., Rich, J., Island, H. D., Marchalik, L., Silver, R., &Zava, D. (2010, August). Four primary temperament dimensions

58
based on neurochemistry. Poster presented at the annual meetingof the American Psychological Association, San Diego, CA.
Fisman, R., Iyengar, S. S., Kamenica, E., & Simonson, I. (2006).
Gender differences in mate selection: Evidence from a speed-dating experiment. Quarterly Journal of Economics, 121, 673–697.
Foa, U. G. (1993). Interpersonal and economic resources. In U. G.
Foa, J. Converse, K. Y. Törnblom, & E. B. Foa (Eds.), Resource
theory: Explorations and applications (pp. 13–30). San Diego,
CA: Academic Press.
Forthofer, M. S., Markman, H. J., Cox, M., Stanley, S., & Kessler, R.
C. (1996). Associations between marital distress and work loss in anational sample. Journal of Marriage and the Family, 58, 597–605.
Fraley, R. C., & Waller, N. G. (1998). Adult attachment patterns: A
test of the typological model. In J. A. Simpson & W. S. Rholes(Eds.), Attachment theory and close relationships (pp. 77–114).
New York, NY: Guilford.
Franiuk, R., Cohen, D., & Pomerantz, E. M. (2002). Implicit theories
of relationships: Implications for relationship satisfaction andlongevity. Personal Relationships, 9, 345–367.
Franiuk, R., Pomerantz, E. M., & Cohen, D. (2004). The causal roleof theories of relationships: Consequences for satisfaction andcognitive strategies. Personality and Social Psychology Bulletin,30, 1494–1507.
Frazzetto, G. (2010). The science of online dating. EMBO Reports,
11, 25–27.
Frost, J. H., Chance, Z., Norton, M. I., & Ariely, D. (2008). People
are experience goods: Improving online dating with virtual dates.
Journal of Interactive Marketing, 22, 51–61.
Furr, R. M. (2008). Framework for profile similarity: Integrating similarity, normativeness, and distinctiveness. Journal of Personality, 76, 1267–1316.
Gagné, F., & Lydon, J. (2001). Mindset and close relationships:
When bias leads to inaccurate predictions. Journal of Personality
and Social Psychology, 81, 85–96.
Galician, M. (2004). Sex, love, and romance in the mass media. Mahwah, NJ: Lawrence Erlbaum.
Gallo, L. C., Troxel, W. M., Matthews, K. A., & Kuller, L. H. (2003).
Marital status and quality in middle-aged women: Associations
with levels and trajectories of cardiovascular risk factors. Health
Psychology, 22, 453–463.
Gangestad, S. W., & Thornhill, R. (1998). Menstrual cycle variation
in women’s preference for the scent of symmetrical men. Proceedings of the Royal Society of London B, 262, 727–733.
Garver-Apgar, C. E., Gangestad, S. W., Thornhill, R., Miller, R. D.,& Olp, J. J. (2006). Major histocompatibility complex alleles,
sexual responsivity, and unfaithfulness in romantic couples. Psychological Science, 17, 830–835.
Gaunt, R. (2006). Couple similarity and marital satisfaction: Are
similar spouses happier? Journal of Personality, 74, 1401–1420.
Gawronski, B., & Bodenhausen, G. V. (2011). The associativepropositional evaluation model: Theory, evidence, and open
questions. In M. P. Zanna & J. M. Olson (Eds.), Advances inexperimental social psychology (Vol. 44, pp. 59–127). Amsterdam, The Netherlands: Elsevier.
Gelles, D. (2011, July 30). Inside Match.com: It’s all about the algorithm. Retrieved from http://www.slate.com/id/2300430/

Finkel et al.
GenePartner.com (2011). Homepage. Available from http://genepartner.com/
Gibbs, J. L., Ellison, N. B., & Lai, C. (2011). First comes love,
then comes Google: An investigation of uncertainty reduction
strategies and self-disclosure in online dating. Communication
Research, 38, 70–100.
Gigaom. (2011, June 14). Match.com picks fight with competitor
Plenty of Fish. Retrieved from http://gigaom.com/2010/04/28/
match-com-picks-fight-with-competitor-plenty-of-fish/
Gillmor, C. S. (2007). Stanford, the IBM 650, and the first trials ofcomputer date matching. IEEE Annals of the History of Computing, 29, 74–80.
Gladue, B. A., & Delaney, H. (1990). Gender differences in perception of attractiveness of men and women in bars. Personality andSocial Psychology Bulletin, 16, 378–391.
Glicksohn, J., & Golan, H. (2001). Personality, cognitive style, and assortative mating. Personality and Individual Differences, 30, 1199–1209.
Goffman, E. (1959). The presentation of self in everyday life. New
York, NY: Doubleday.
Gollwitzer, P. M., & Bayer, U. (1999). Deliberative versus implemental mindsets in the control of action. In S. Chaiken & Y.
Trope (Eds.), Dual-process theories in social psychology (pp.
403–422). New York, NY: Guilford.
Gonzaga, G. (2011). How you meet your spouse matters. Retrieved fromhttp://advice.eharmony.com/blog/2011/02/10/how-you-meetyour-spouse-matters/
Gonzaga, G. C., Campos, B., & Bradbury, T. (2007). Similarity, convergence, and relationship satisfaction in dating and married couples. Journal of Personality and Social Psychology, 93, 34–48.
Gonzaga, G. C., Carter, S., & Buckwalter, G. J. (2010). Assortative
mating, convergence, and satisfaction in married couples. Personal Relationships, 17, 634–644.
Gonzalez-Vallejo, C., & Moran, E. (2001). The evaluability hypothesis revisited: Joint and separate evaluation preference reversal asa function of attribute importance. Organizational Behavior andHuman Decision Processes, 86, 216–233.
Goodwin, R. (1990). Dating agency members: Are they “different”?
Journal of Social and Personal Relationships, 7, 423–430.
Gotlib, I. H., Lewinsohn, P. M., & Seeley, J. R. (1998). Consequencesof depression during adolescence: Marital status and maritalfunctioning in early adulthood. Journal of Abnormal Psychology,
107, 686–690.
Gottlieb, L. (2006, March). How do I love thee? The Atlantic Monthly,
58-70. Retrieved from http://www.theatlantic.com/doc/200603/
online-loveGottman, J. M. (1994). What predicts divorce? The relationship between
marital processes and marital outcomes. Hillsdale, NJ: Erlbaum.
Gottman, J. M., Coan, J., Carrere, S., & Swanson, C. (1998). Predicting marital happiness and stability from newlywed interactions.
Journal of Marriage and the Family, 60, 5–22.
Grob, B., Knapp, L. A., Martin, R. D., & Anzenberger, G. (1998). The
major histocompatibility complex and mate choice: Inbreeding
avoidance and selection of good genes. Experimental and Clinical Immunogenetics, 15, 119–129.
Gwinnell, E. (1998). Online seductions: Falling in love with strangers on the Internet. New York, NY: Kodansha America.

Online Dating
Hancock, J. T., Thom-Santelli, J., & Ritchie, T. (2004). Deception
and design: The impact of communication technology on lying
behavior. Proceedings, Conference on Computer Human Interaction, 6, 129–134.
Harmon, A. (2003). Online dating sheds its stigma as losers.com. The New
York Times. Retrieved from http://www.nytimes.com/2003/06/29/us/
online-dating-sheds-its-stigma-as-loserscom.html
Havlicek, J., & Roberts, S. C. (2009). MHC-correlated mate choicein humans: A review. Psychoneuroendocrinology, 34, 497–512.
Hazan, C., & Shaver, P. R. (1987). Romantic love conceptualized as
an attachment process. Journal of Personality and Social Psychology, 52, 511–524.
Heaton, T. B., & Pratt, E. L. (1990). The effects of religious homogamy on marital satisfaction and stability. Journal of Family
Issues, 11, 191–207.
Heino, R., Ellison, N., & Gibbs, J. (2010). Relationshopping: Investigating the market metaphor in online dating. Journal of Socialand Personal Relationships, 27, 427–447.
Hendrick, C., & Brown, S. R. (1971). Introversion, extraversion, andinterpersonal attraction. Journal of Personality and Social Psychology, 20, 31–36.
Henry-Waring, M., & Barraket, J. (2008). Dating & intimacy in the
21st century: The use of online dating sites in Australia. International Journal of Emerging Technologies and Society, 6, 14–33.
Heyman, J. E., Orhun, Y., & Ariely, D. (2004). Auction fever: The
effect of opponents and quasi-endowment on product valuations.
Journal of Interactive Marketing, 18, 7–21.
Heyman, R. E., & Slep, A. M. S. (2001). The hazards of predicting divorce without crossvalidation. Journal of Marriage and theFamily, 63, 473–479.
Hill, R. (1949). Families under stress. New York, NY: Harper & Row.
Hiltz, S. R., Johnson, K., & Turoff, M. (1986). Experiments in groupdecision making communication process and outcome in face-toface versus computerized conferences. Human Communication
Research, 13, 225–252.
Hitsch, G. J., Hortaçsu, A., & Ariely, D. (2010a). Matching and sorting in online dating. American Economic Review, 100, 130–163.
Hitsch, G. J., Hortaçsu, A., & Ariely, D. (2010b). What makes you
click? Mate preferences in online dating. Quantitative Marketingand Economics, 8, 393–427.
Hogan, B., Dutton, W., & Li, N. (2011). A global shift in the social
relationships of networked individuals: Meeting and dating online
comes of age. Retrieved from http://ssrn.com/abstract=176388
Hohenboken, W. D. (1985). The manipulation of variation in quantitative traits: A review of possible genetic strategies. Journal ofAnimal Science, 60, 101–110.
Holland, A. S., Fraley, R. C., & Roisman, G. I. (2011). Attachment styles
in dating couples: Predicting relationship functioning over time.
Personal Relationships. doi:10.1111/j.1475-6811.2011.01350.x
Holmes, B. M. (2007). In search of my “one and only”: Romanceoriented media and beliefs in romantic relationships destiny.
Electronic Journal of Communication, 17, 1–29.
Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta-analytic review. PLoS Medicine,7, e1000316. doi:10.1371/journal.pmed.1000316

59
Houran, J., Lange, R., Rentfrow, J. P., & Bruckner, K. H. (2004). Do
online matchmaking tests work? An assessment of preliminary
evidence for a publicized “predictive model of marital success.”
North American Journal of Psychology, 6, 507–526.
Houts, R. M., Robins, E., & Huston, T. L. (1996). Compatibility andthe development of premarital relationships. Journal of Marriage
and the Family, 58, 7–20.
Hsee, C. K., Loewenstein, G. F., Blount, S., & Bazerman, M. H.
(1999). Preference reversals between joint and separate evaluations of options: A review and theoretical analysis. Psychological
Bulletin, 125, 576–590.
Hsee, C. K., & Zhang, J. (2010). General evaluability theory. Perspectives on Psychological Science, 5, 343–355.
Humphries, C. (2010). Dating sites try adaptive matchmaking. Technology Review. Retrieved from http:/www.technologyreview.
com/printer_friendly_article.aspx?id=26805
Huston, T. L. (2000). The social ecology of marriage and other intimate unions. Journal of Marriage and the Family, 62, 298–320.
Internet Business Law Services. (2011, March 31). Internet law: DoesIMBRA exclusively regulate online dating sites and protect onlinedating U.S. citizen brides? Retrieved from http://www.ibls.com/
internet_law_news_portal_view.aspx?id=2083&;s=latestnews
InternetWorldStats.com. (2011). Internet world stats: Usage andpopulation statistics. Retrieved August 31 from http://internetworldstats.com/stats.htm.
Iyengar, S. S. (2010). The art of choosing. New York, NY: Twelve
Publishers, an imprint of Hachette Book Group.
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating:
Can one desire too much of a good thing? Journal of Personality
and Social Psychology, 79, 995–1006.
Iyengar, S. S., Simonson, I., Fisman, R., & Mogilner, C. (2005, January). I know what I want but can I find it? Examining the dynamic
relationship between stated and revealed preferences. Paper session presented at the Society for Personality and Social Psychology Annual Meeting, New Orleans, LA.
Jacobson, D. (1999). Impression formation in cyberspace: Online
expectations and offline experiences in text-based virtual communities. Journal of Computer-Mediated Communication, 5, 0.
Jacobson, N. S., & Christensen, A. (1996). Integrative couple therapy: Promoting acceptance and change. New York, NY: Norton.
JDate.com. (2011). Homepage. Available from www.jdate.com
Jiang, L., Bazarova, N. N., & Hancock, J. T. (2011). The disclosure–
intimacy link in computer mediated communication: An attributional extension of the hyperpersonal model. Human Communication Research, 37, 58–77.
Jinks, J. L., & Fulker, D. W. (1970). A comparison of the biometrical-genetical, MAVA and classical approaches to the analysis ofhuman behavior. Psychological Bulletin, 73, 311–349.
Johnson, M. D., Cohan, C. L., Davila, J., Lawrence, E., Rogge, R. D.,
Karney, B. R., . . . Bradbury, T. N. (2005). Problem-solving skills
and affective expressions as predictors of change in marital satisfaction. Journal of Consulting and Clinical Psychology, 73, 15–27.
Joinson, A. N. (2001). Self-disclosure in computer mediated communication: The role of self-awareness and visual anonymity.
European Journal of Social Psychology, 31, 177–192.

60
Journal of the American Medical Association. (2011, May 18). JAMA
instructions for authors. Retrieved from http://jama.ama-assn.
org/site/misc/ifora.xhtml
Kahn, J. (2011, July 11-18). The visionary. The New Yorker, 87, 46–53.
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1990). Experimental
tests of the endowment effect and the Coase theorem. Journal of
Political Economy, 98, 1325–1348.
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies:
The endowment effect, loss aversion, and status quo bias. Journalof Economic Perspectives, 5, 193–206.
Kaplan, M. (2008). You have 4 minutes to choose your perfect mate.
Nature, 451, 760–762.
Karney, B. R., & Bradbury, T. N. (1995). The longitudinal course ofmarital quality and stability: A review of theory, methods, andresearch. Psychological Bulletin, 118, 3–34.
Karney, B. R., & Bradbury, T. N. (1997). Neuroticism, marital interaction, and the trajectory of marital satisfaction. Journal of Personality and Social Psychology, 72, 1075–1092.
Keller, K. L., & Staelin, R. (1987). Effects of quality and quantityof information on decision effectiveness. Journal of Consumer
Research, 14, 200–213.
Kelly, E. L., & Conley, J. J. (1987). Personality and compatibility: A
prospective analysis of marital stability and marital satisfaction.
Journal of Personality and Social Psychology, 52, 27–40.
Kenny, D. A., & Acitelli, L. K. (1994). Measuring similarity in couples. Journal of Family Psychology, 8, 417–431.
Kenny, D. A., Kashy, D. A., & Cook, W. L. (2006). Dyadic data analysis. New York, NY: Guilford.
Kerckhoff, A. C. (1964). Patterns of homogamy and the field of eligibles. Social Forces, 42, 289–297.
Kiecolt-Glaser, J. K., Loving, T. J., Stowell, J. R., Malarkey, W. B.,
Lemeshow, S., Dickinson, S. L., & Glaser, R. (2005). Hostile
marital interactions, proinflammatory cytokine production, andwound healing. Archives of General Psychiatry, 62, 1377–1384.
Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychologicalaspects of computer-mediated communication. American Psychologist, 39, 1123–1134.
Kim, M., Kwon, K., & Lee, M. (2009). Psychological characteristics of ODS user: The effect of self-esteem, involvement, andsociability on the use of ODSs. CyberPsychology & Behavior,
12, 445–449.
King, A. E., Austin-Oden, D., & Lohr, J. M. (2009). Browsing forlove in all the wrong places: Does research show that Internet
matchmaking is more successful than traditional dating? Skeptic,
15, 48–55.
Klohnen, E. C., & Luo, S. (2003). Interpersonal attraction andpersonality: What is attractive-Self similarity, ideal similarity,
complementarity or attachment security? Journal of Personality
and Social Psychology, 85, 709–722.
Klohnen, E. C., & Mendelsohn, G. A. (1998). Partner selection for
personality characteristics: A couple-centered approach. Personality and Social Psychology Bulletin, 24, 268–278.
Knee, C. R. (1998). Implicit theories of relationships: Assessment andprediction of romantic relationship initiation, coping, and longevity. Journal of Personality and Social Psychology, 74, 360–370.

Finkel et al.
Knee, C. R., & Bush, A. (2008). Relationship beliefs and their role
in relationship initiation. In S. Sprecher, A. Wenzel, & J. Harvey(Eds.), The handbook of relationship initiation (pp. 471–485).
Hillsdale, NJ: Erlbaum.
Komblum, J. (May 18, 2005). eHarmony: Heart and soul. Retrieved fromhttp://www.usatoday.com/life/people/2005-05-18-eharmony_x.htm
Kreider, T. (May 13, 2011). True stories: Getting offline. Retrieved fromhttp://www.nerve.com/love-sex/true-stories/true-stories-gettingoffline
Kruglanski, A. W., Thompson, E. P., Higgins, E. T., Atash, M. N.,
Pierro, A., Shah, J. Y., & Spiegel, S. (2000). To “do the rightthing” or to “just do it”: Locomotion and assessment as distinct
self-regulatory imperatives. Journal of Personality and Social
Psychology, 79, 793–815.
Kumashiro, M., Rusbult, C. E., Finkenauer, C., & Stocker, S. (2007).
To think or to do: Assessment orientation, locomotion orientation, and the Michelangelo Phenomenon. Journal of Social andPersonal Relationships, 24, 591–611.
Langer, E. J., & Rodin, J. (1976). The effects of choice and enhanced personal responsibility for the aged: A field experiment in an institutionalsetting. Journal of Personality and Social Psychology, 34, 191–198.
Laumann, E. O., Gagnon, J. H., Michael, R. T., & Michaels, S.
(1994). The social organization of sexuality: Sexual practices in
the United States. Chicago, IL: University of Chicago Press.
Lawrence, R. (2004). Guest editorial: Overcoming the stigma of
online dating-What are we embarrassed for? Online Dating Magazine. Retrieved from http://www.onlinedatingmagazine.com/
columns/editorials/editorial10.html
Lawson, H. M., & Leck, K. (2006). Dynamics of Internet dating.
Social Science Computer Review, 24, 189–208.
Le, B., & Agnew, C. R. (2003). Commitment and its theorized determinants: A meta-analysis of the investment model. Personal
Relationships, 10, 37–57.
Le, B., Dove, N. L., Agnew, C. R., Korn, M. S., & Mutso, A. A.
(2010). Predicting non-marital romantic relationship dissolution:
A meta-analytic synthesis. Personal Relationships, 17, 377–390.
Lea, M., & Spears, R. (1995). Love at first byte? Building personal
relationships over computer networks. In J. T. Wood & S. W.
Duck (Eds.), Understudied relationships: Off the beaten track(pp. 197–233). Newbury Park, CA: SAGE.
Ledbetter, A. M. (2008). Chronemic cues and sex differences in relational e-mail. Social Science Computer Review, 26, 466–482.
Lee, L., Loewenstein, G., Ariely, D., Hong, J., & Young, J. (2008). If
I’m not hot, are you hot or not: Physical attractiveness evaluationand dating preferences as a function of one’s own attractiveness.
Psychological Science, 19, 669–677.
Lenton, A. P., Fasolo, B., & Todd, P. M. (2008). “Shopping” for amate: Expected vs. experienced preferences in online mate choice.
IEEE Transactions on Professional Communication, 51, 169–182.
Lenton, A. P., Fasolo, B., & Todd, P. M. (2009). The relationshipbetween number of potential mates and mating skew in humans.
Animal Behaviour, 77, 55–60.
Lenton, A. P., & Francesconi, M. (2010). How humans cognitively
manage an abundance of mate options. Psychological Science,
21, 528–533.

Online Dating
Lenton, A. P., & Francesconi, M. (2011). Too much of a good thing?
Variety is confusing in mate choice. Biology Letters, 7, 528–531.
Lenton, A. P., & Stewart, A. (2008). Changing her ways: Number of
options and mate standard strength impact mate choice strategy
and satisfaction. Judgment and Decision Making, 3, 501–511.
Leonhardt, D. (2006, March 28). The famous founder of operation
match. The New York Times. Retrieved from http:/www.nytimes.com/2006/03/28/business/29leonside.html?_r=1
Leotti, L. A., & Delgado, M. R. (2011). The inherent reward of
choice. Psychological Science, 22, 1310–1318.
Leotti, L. A., Iyengar, S. S., & Ochsner, K. N. (2010). Born to choose:
The origins and value of the need for control. Trends in Cognitive
Science, 14, 457–463.
Leslie, B., & Morgan, M. (2009). Soulmates, compatibility and intimacy:
Allied discursive resources in the struggle for relationship satisfaction in the new millennium. New Ideas in Psychology, 29, 10–23.
Levine, R., Sato, S., Hashimoto, T., & Verma, J. (1995). Love andmarriage in eleven cultures. Journal of Cross-Cultural Psychology, 26, 554–571.
Levinger, G. (1986). Compatibility in relationships. Social Science,
71, 173–177.
Lewak, R. W., Wakefield, J. A., Jr., & Briggs, P. F. (1985). Intelligence and personality in mate choice and marital satisfaction.
Personality and Individual Differences, 6, 471–477.
Little, A. C., & Perrett, D. I. (2002). Putting beauty back in the eye ofthe beholder: Evolution and individual differences in face preference. Psychologist, 15, 28–32.
LittleRedRails.com. (2011a). What is the cost of eHarmony subscriptions? Retrieved from http://www.littleredrails.com/blog/whatis-the-price-of-eharmony-subscriptions/
LittleRedRails.com. (2011b). What is the cost of Match.com subscriptions? Retrieved from http://www.littleredrails.com/
blog/2010/06/07/what-is-the-price-of-match-com-subscriptions/Long, B. L. (2010). Scripts for online dating: A model and theoryof online romantic relationship initiation (Unpublished doctoral
dissertation). Bowling Green State University, OH.
Lopoo, L. M., & Western, B. (2005). Incarceration and the formationand stability of marital unions. Journal of Marriage and Family,
67, 721–734.
Luo, S., Chen, H., Yue, G., Zhang, G., Zhaoyang, R., & Xu, D.
(2008). Predicting marital satisfaction from self, partner, andcouple characteristics: Is it me, you, or us? Journal of personality, 76, 1231–1266.
Luo, S., & Klohnen, E. C. (2005). Assortative mating and marital
quality in newlyweds: A couple-centered approach. Journal of
Personality and Social Psychology, 88, 304.
Lutz-Zois, C. J., Bradley, A. C., Mihalik, J. L., & Moorman-Eavers,
E. R. (2006). Perceived similarity and relationship success among
dating couples: An idiographic approach. Journal of Social andPersonal Relationships, 23, 865–880.
Lye, D. N., & Biblarz, T. J. (1993). The effects of attitudes toward
family life and gender roles on marital satisfaction. Journal of
Family Issues, 14, 157–188.
Lykken, D. T., & Tellegen, A. (1993). Is human mating adventitious
or the result of lawful choice? A twin study of mate selection.
Journal of Personality and Social Psychology, 65, 56–68.

61
Madden, M., & Lenhart, A. (2006). Online dating. Pew Internet &American Life Project. Retrieved from http://www.pewinternet.org/Reports/2006/Online-Dating.aspx
Malhotra, N. K. (1982). Information load and consumer decision
making. Journal of Consumer Research, 8, 419–430.
Malouff, J. M., Rooke, S. E., & Schutte, N. S. (2008). The heritabilityof human behavior: Results of aggregating meta-analyses. Current Psychology, 27, 153–161.
Malouff, J. M., Thorsteinsson, E. B., Schutte, N. S., Bhullar, N., &Rooke, S. E. (2010). The five-factor model of personality andrelationship satisfaction of intimate partners: A meta-analysis.
Journal of Research in Personality, 44, 124–127.
Manning, J. T. (2002). Digit ratio: A pointer to fertility, behaviour,and health. New Brunswick, NJ: Rutgers University Press.
Mare, R. (1991). Five decades of educational assortative mating.
American Sociological Review, 56, 15–32.
Marist Poll. (2011). Retrieved from http://maristpoll.marist.edu/210its-destiny-most-americans-believe-in-soul-mates/
Markey, P. M., Lowmaster, S., & Eichler, W. (2010). A real timeassessment of interpersonal complementarity. Personal Relationships, 17, 13–25.
Markey, P. M., & Markey, C. N. (2007). Romantic ideals, romantic
obtainment, and relationship experiences. Journal of Social andPersonal Relationships, 24, 517–533.
Match.com. (2011). Retrieved from http://www.match.com/index.aspx
Mathews, T. J. (1965). Operation match. The Harvard Crimson.
Retrieved from http://www.thecrimson.com/article/1965/11/3/
operation-match-pif-you-stop-to/
McCrae, R. R., & Costa, P. T. (1997). Personality trait structure as ahuman universal. American Psychologist, 52, 509–516.
McGrane, S. (2009, June 29). Can genetics help you find love? Time
Magazine. Retrieved from http://www.time.com/time/magazine/
article/0,9171,1905505,00.html
McKenna, K. Y. A., Green, A. S., & Gleason, M. E. J. (2002). Relationship formation on the Internet: What’s the big attraction?
Journal of Social Issues, 58, 9–31.
McLanahan, S., & Sandefur, G. (1994). Growing up with a single
parent: What hurts, what helps? Cambridge, MA: Harvard University Press.
McNulty, J. K. (in press). Personality and relationships. In J. A.
Simpson (Ed.), Oxford handbook. New York, NY: Oxford University Press.
Merkle, E. R., & Richardson, R. A. (2000). Digital dating and virtual
relating: Conceptualizing computer mediated romantic relationships. Family Relations, 49, 187–192.
Merskin, D. L., & Huberlie, M. (1996). Companionship in the classifieds: The adoption of personal advertisements by daily newspapers. Journalism & Mass Communication Quarterly, 77, 219–229.
Milgram, S. (1984). Network love. Omni, 7(34), 410–413.
Miller, R. S. (1997). Inattentive and contented: Relationship commitment and attention to alternatives. Journal of Personality andSocial Psychology, 73, 758–766.
Mitchell, R. L. (2009). Online dating: Analyzing the algorithms of
attraction. PCWorld. Retrieved from http://www.pcworld.com/
article/159884-2/online_dating_analyzing_the_algorithms_of_
attraction.html

62Monga, M., Alexandrescu, B., Katz, S. E., Stein, M., & Ganiats, T.
(2004). Impact of infertility on quality of life, marital adjustment,
and sexual function. Urology, 63, 126–130.
Montoya, R. M., Horton, R. S., & Kirchner, J. (2008). Is actual similarity necessary for attraction? A meta-analysis of actual and perceived similarity. Journal of Social and Personal Relationships,
25, 889–922.
Morry, M. M., Kito, M. I. E., & Ortiz, L. (2011). The attraction-similarity model and dating couples: Projection, perceived similarity,and psychological benefits. Personal Relationships, 18, 125–143.
Nagoshi, C. T., Johnson, R. C., & Honbo, K. A. M. (1992). Assortative mating for cognitive abilities, personality, and attitudes: Offspring from the Hawaii family study of cognition. Personality
and Individual Differences, 13, 883–891.
National Advertising Review Council (NARC). (2011). NARC
online case reports. Retrieved from http://www.narcpartners.org/
reports/CaseReports.aspx
Neate, R. (2009, September 5). Appliance of science in finding love
online. The Telegraph. Retrieved from http://www.telegraph.co.uk/finance/newsbysector/retailandconsumer/6142658/Applianceof-science-in-finding-love-online.html.
Neff, L. A., & Karney, B. R. (2009). Stress and reactivity to daily
relationship experiences: How stress hinders adaptive processes
in marriage. Journal of Personality and Social Psychology, 97,
435–450.
Newcomb, T. M. (1956). The prediction of interpersonal attraction.
American Psychologist, 11, 575.
Nezu, A. M., & Nezu, C. M. (2008). Evidence-based outcome
research: A practical guide to conducting randomized controlled
trials for psychosocial interventions. Oxford, UK: Oxford University Press.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2, 175–220.
Nielsenwire.com. (2009, February 11). Online dating and advertising prove to be a good match. Retrieved from http://blog.nielsen.com/nielsenwire/consumer/online-dating-and-advertisingprove-to-be-a-good-match/
Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we canknow: Verbal reports on mental processes. Psychological Review,
84, 231–259.
Nunes, J. C., & Dreze, X. (2006). The endowed progress effect: How
artificial advancement increases effort. Journal of Consumer
Research, 32, 504–512.
OkCupid.com. (2011). About us. Retrieved from http://www.okcupid.com/about/okcupid
Okdie, B. M., Guadagno, R. E., Bernieri, F. J., Geers, A. L., &Mclarney-Vesotski, A. R. (2011). Getting to know you: Face-toface versus online interactions. Computers in Human Behavior,27, 153–159.
Open.nystate.gov. (2011). S3618-2011: Enacts the “internet dating
safety act of 2011.” Retrieved January 19, 2012, from http://open.nystate.gov/legislation/bill/S3618-2011
Orenstein, S. (2003, August 1). The love algorithm. Retrieved fromhttp://money.cnn.com/magazines/business2/business2_archive/
2003/08/01/346313/index.htm.

Finkel et al.
Orr, A. (2004). Meeting, mating, and cheating: Sex, love, and the newworld of online dating. Upper Saddle River, NJ: Reuters PrenticeHall.
Overall, N. C., Sibley, C. G., & Tan, R. (2011). The costs and benefitsof sexism: Resistance to influence during relationship conflict.
Journal of Personality and Social Psychology, 101, 271–290.
Parks, M. R., & Floyd, K. (1996). Making friends in cyberspace.
Journal of Communication, 46, 80–97.
Parks, M. R., & Roberts, L. D. (1998). “Making MOOsic”: The
development of personal relationships online and a comparison totheir off-line counterparts. Journal of Social and Personal Relationships, 15, 517–537.
Paumgarten, N. (2011, July 4). Looking for someone: Sex, love andloneliness on the Internet. The New Yorker, 87, pp. 36–49.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive
decision maker. Cambridge, England: Cambridge University Press.
Peel, L. (2008, Oct 6). Matchmaker, Matchmaker, make me amatch…if the algorithms agree. Retrieved from http://business.timesonline.co.uk/tol/business/industry_sectors/technology/
article4887501.ece
Penn, D. J. (2002). The scent of genetic compatibility: Sexual selection and the major histocompatibility complex. Ethology andSociobiology, 108, 1–21.
Pennebaker, J. W., Dyer, M. A., Caulkins, R. S., Litowitz, D. L., Ackreman, P. L., Anderson, D. B., & McGraw, K. M. (1979). Don’t the
girls get prettier at closing time: A country and western application to psychology. Personality and Social Psychology Bulletin, 5,
122–125.
PerfectMatch.com. (2011a, June 15). Retrieved from http://www.perfectmatch.com/
PerfectMatch.com. (2011b, June 15). Frequentlyaskedquestions.
Retrieved from http://www.perfectmatch.com/faq.asp
Pinker, S. (2002). The blank slate: The modern denial of humannature. New York, NY: Viking.
Plassman, H., O’Doherty, J., Shiv, B., & Rangel, A. (2008). Marketing
actions can modulate neural representations of experienced pleasantness. Proceedings of the National Academy of Sciences, 105,
1050–1054.
PlentyOfFish.com. (2011, June 15). Retrieved from http://www.Plenty
OfFish.com/
Ramirez, A., & Wang, Z. (2008). When online meets offline: An
expectancy violations theory perspective on modality switching.
Journal of Communication, 58, 20–39.
Ramirez, A., & Zhang, S. (2007). When online meets offline: The
effect of modality switching on relational communication. Communication Monographs, 74, 287–310.
Ranganath, K. A., Smith, C. T., & Nosek, B. A. (2008). Distinguishing automatic and controlled components of attitudes from direct
and indirect measurement methods. Journal of Experimental
Social Psychology, 44, 386–396.
Redelmeier, D. A., & Shafir, E. (1995). Medical decision making insituations that offer multiple alternatives. Journal of the American Medical Association, 273, 302–305.
Reis, H. T. (2012). A brief history of relationship research in social
psychology. In A. W. Kruglanski & W. Stroebe (Eds.), Handbook

Online Datingof the history of social psychology (pp. 363–382). New York, NY:
Psychology Press.
Reis, H. T., Maniaci, M. R., Caprariello, P. A., Eastwick, P. W., &Finkel, E. J. (2011a). Familiarity does indeed lead to attraction
in live interaction. Journal of Personality and Social Psychology,
101, 557–570.
Reis, H. T., Maniaci, M. R., Caprariello, P. A., Eastwick, P. W., &Finkel, E. J. (2011b). In live interaction, does familiarity promote attraction or contempt? A reply to Norton, Frost, and Ariely (2011). Journal of Personality and Social Psychology, 101,
575–578.
Reis, H. T., & Shaver, P. (1988). Intimacy as an interpersonal process. In S. Duck (Ed.), Handbook of personal relationships (pp.
367–389). Chichester, UK: Wiley.
Rice, R. E., & Love, G. (1987). Electronic emotion: Socioemotional
content in a computer-mediated communication network. Communication Research, 14, 85–108.
Rick, S., Small, D., & Finkel, E. (2011). Fatal (fiscal) attraction:
Spendthrifts and tightwads in marriage. Journal of Marketing
Research, XLVIII, 228–237.
Roberts, S. C., & Little, A. C. (2008). Good genes, complementary
genes and human mate choice. Genetica, 132, 309–321.
Roberts, S. C., Little, A. C., Gosling, L. M., Jones, B. C., Perrett,
D. I., Carter, V., & Petrie, M. (2005). MHC-assortative facial
preferences in humans. Biology Letters, 1, 400–403.
Robins, R. W., Caspi, A., & Moffitt, T. E. (2000). Two personalities, one relationship: Both partners’ personality traits shape thequality of their relationship. Journal of Personality and Social
Psychology, 79, 251–259.
Robins, R. W., Caspi, A., & Moffitt, T. E. (2002). It’s not just who
you’re with, it’s who you are: Personality and relationship experiences across multiple relationships. Journal of Personality, 70,
925–964.
Rogge, R. D., Bradbury, T. N., Hahlweg, K., Engl, J., & Thurmaier, F.
(2006). Predicting marital distress and dissolution refining the twofactor hypothesis. Journal of Family Psychology, 20, 156–159.
Roisman, G. I., Collins, W. A., Sroufe, L. A., & Egeland, B. (2005).
Predictors of young adults’ representations of and behavior in
their current romantic relationship: Prospective tests of the prototype hypothesis. Attachment & Human Development, 7, 105–121.
Rosen, L. D., Cheever, N. A., Cummings, C., & Felt, J. (2008). The
impact of emotionality and self-disclosure on online dating versus
traditional dating. Computers in Human Behavior, 24, 2124–2157.
Rosenfeld, M. J. (2010, April). Meeting online: The rise of the Internet as a social intermediary. Paper session presented at the Population Association of America Meetings, Dallas, TX.
Rosenfeld, M. J., & Thomas, R. J. (2010, September). Meeting
online: The rise of the Internet as a social intermediary. Unpublished manuscript, Department of Sociology, Stanford University,
Stanford, CA
Rowan, D. (2011, April 25). How Badoo built a billion-pound social
network … on sex. Wired. Retrieved from http://www.wired.co.uk/
magazine/archive/2011/05/features/sexual-network?page=all
Rudder, C. (2010, April 7). Why you should never pay for online dating. Retrieved from http://interestingreads.posterous.com/whyyou-should-never-pay-for-online-dating-ok

63
Rule, N. O., & Ambady, N. (2008). The face of success: Inferencesfrom chief executives officers’ appearance predict company profits. Psychological Science, 19, 109–111.
Rusbult, C. E. (1983). A longitudinal test of the investment model:
The development (and deterioration) of satisfaction and commitment in heterosexual involvements. Journal of Personality andSocial Psychology, 45, 101–117.
Russell, R. J. H., & Wells, P. A. (1991). Personality similarity and qualityof marriage. Personality and Individual Differences, 12, 407–412.
Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic wellbeing. Annual Review of Psychology, 52, 141–166.
Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision
making. Journal of Risk and Uncertainty, 1, 7–59.
Sautter, J., Tippett, R. M., & Morgan, S. P. (2010). The social demography of Internet dating in the United States. Social Science
Quarterly, 91, 554–575.
Sbarra, D. A., Law, R. W., & Portley, R. M. (2011). Divorce anddeath: A meta-analysis and research agenda for clinical, social,and health psychology. Perspectives on Psychological Science,
6, 454–474.
Scheibehenne, B., Greifeneder, R., & Todd, P. M. (2010). Can thereever be too many options? A meta-analytic review of choice overload. Journal of Consumer Research, 37, 409–425.
Schellenberg, J. A. (1960). Homogamy in personal values and the“field of eligibles.” Social Forces, 39, 157–162.
Schöndienst, V., & Dang-Xuan, L. (2011). The role of linguistic properties in online dating communication—A large-scale study of contact
initiation messages. Proceedings of the 15th Pacific Asia Conference on Information Systems, Paper 166, Brisbane, Australia.
Schouten, A. P., Valkenburg, P. M., & Peter, J. (2009). An experimental test of processes underlying self-disclosure in computermediated communication. Cyberpsychology, 3, 1–15.
Schwartz, B. (2004). The paradox of choice: Why more is less. New
York, NY: Eco/Harper-Collins Publishers.
Schwartz, P. (2006). Finding your perfect match. New York, NY:
Penguin Group.
ScientificMatch.com. (2011, July 30). Home Page. Retrieved fromhttps://scientificmatch.com/html/index.php
Shalit, G. (1966, February 22). Boy…girl…computer. New datingcraze sweeps the campus. Look Magazine. Retrieved from http://
blog.modernmechanix.com/2008/0/08/boy-girl-computer/
Shambora, J. (2010, September 23). eHarmony’s algorithm of love.
Retrieved from http://tech.fortune.cnn.com/2010/09/23/thealgorithm-of-love
Shin, J., & Ariely, D. (2004). Keeping doors open: The effect ofunavailability on incentives to keep options viable. Management
Science, 50, 575–586.
Short, J., Williams, E., & Christie, B. (1976). The social psychologyof telecommunications. London, England: John Wiley.
Siegel, J., Dubrovsky, V., Kiesler, S., & McGuire, T. W. (1986).
Group processes in computer-mediated communication. Organizational Behavior and Human Decision Processes, 37,
157–187.
Simenauer, J., & Carroll, D. (1982). Singles: The new Americans.
New York, NY: Simon & Schuster.

64
Simons, R. L. (1996). Understanding differences between divorced
and intact families: Stress, interaction, and child outcomes.
Thousand Oaks, CA: SAGE.
Simonson, I., & Tversky, A. (1992). Choice in context: Tradeoff contrast and extreme aversion. Journal of Marketing Research, 29,
281–295.
Simpson, J. A., Campbell, B., & Berscheid, E. (1986). The association between romantic love and marriage: Kephart (1967) twice
revisited. Personality and Social Psychology Bulletin, 12, 363–
372.
Skopek, J., Schulz, F., & Blossfeld, H. (2011). Who contacts whom?
Educational homophily in online mate selection. European Sociological Review, 27, 180–195.
Smaill, B. (2004). Online personals and narratives of the self: Australia’s RSVP. Convergence: The Journal of Research into NewMedia Technologies, 10, 93–107.
Snyder, M., & Swann, W. B. (1978). Hypothesis-testing processes insocial interaction. Journal of Personality and Social Psychology,
36, 1202–1212.
Spears, R., & Lea, M. (1992). Social influence and the influence of
the “social” in computer-mediated communication. In M. Lea(Ed.), Contexts of computer-mediated communication (pp. 30–
65). Hemel Hempsted, UK: Wheatsheaf.
Sprecher, S. (1989). The importance to males and females of physical attractiveness, earning potential and expressiveness in initialattraction. Sex Roles: A Journal of Research, 21, 591–607.
Sprecher, S., & Duck, S. (1994). Sweet talk: The importance of perceived communication for romantic and friendship attraction
experienced during a get-acquainted date. Personality and Social
Psychology Bulletin, 20, 391–400.
Sprecher, S., Felmlee, D., Orbuch, T. L., & Willetts, M. C. (2002).
Social networks and change in personal relationships. In A. L.
Vangelisti, H. T. Reis, & M. A. Fitzpatrick (Eds.), Stability andchange in relationships (pp. 257–284.). New York, NY: Cambridge University Press.
State.nj.us. (2011). Internet dating safety act. Retrieved from www.nj.gov/lps/ca/laws/InternetDatingAct.pdf
Steffek, L. M., & Loving, T. J. (2009). Contemporary methods ofsocial introduction: Is the stigmatization justified? Interpersona,3, 87–104.
Stephure, R. J., Boon, S. D., MacKinnon, S. L., & Deveau, V. L.
(2009). Internet initiated relationships: Associations between age
and involvement in online dating. Journal of Computer-MediatedCommunication, 14, 658–681.
Stone, A. (2011). Match.com to begin checking users against sex
offender database. ABC News. Retrieved from http://abcnews.go.com/US/match-begin-checking-users-sex-offender-database/story?id=13396930
Subscription Site Insider. (2011). Dating and matchmaking site
benchmark report. Newport, RI: Anne Holland Ventures.
Sunnafrank, M. (1988). Predicted outcome value in initial conversations. Communication Research Reports, 5, 169–172.
Taylor, S. E., & Gollwitzer, P. M. (1995). Effects of mindset on positive illusions. Journal of Personality and Social Psychology, 69,
213–226.

Finkel et al.
Teachman, J. (2010). Work-related health limitations, education, andthe risk of marital disruption. Journal of Marriage and Family,
72, 919–932.
Terman, L. M. (1938). Psychological factors in marital happiness.
New York, NY: McGraw-Hill.
Terman, L. M. (1948). Marital adjustment and its prediction. In M.
Fishbein & E. W. Burgess (Eds.), Successful marriage (pp. 117–
131). Garden City, NY: Doubleday.
Thaler, R. H. (1980). Toward a positive theory of consumer choice.
Journal of Economic Behavior & Organization, 1, 39–60.
Thibaut, J. W., & Kelley, H. H. (1959). The social psychology ofgroups. New York, NY: Wiley.
Thompson, A., & Bolger, N. (1999). Emotional transmission in couples under stress. Journal of Marriage and the Family, 61, 38–48.
Thompson, M., Zimbardo, P., & Hutchinson, G. (2005). Consumers are having second thoughts about online dating: Are the real
benefits getting lost in over promises? Unpublished manuscript.
Available from http://weattract.com/
Thornhill, R., Gangestad, S. W., Miller, R., Scheyd, G., McCollough,J. K., & Franklin, M. (2003). Major histocompatibility complex genes, symmetry, and body scent attractiveness in men andwomen. Behavioral Ecology, 14, 668–678.
Tidwell, L. C., & Walther, J. B. (2002). Computer-mediated communication effects on disclosure, impressions, and interpersonalevaluations: Getting to know one another a bit at a time. Human
Communication Research, 28, 317–348.
Tidwell, N. D., Eastwick, P. W., & Finkel, E. J. (in press). Perceived,
not actual, similarity predicts initial attraction in a live romantic context: Evidence from the speed-dating paradigm. Personal
Relationships.
Tierney, J. (2008). Hitting it off, thanks to the algorithms of love.
The New York Times. Retrieved from http://www.nytimes.com/2008/01/29/science/29tier.html
Till, A., & Freedman, E. M. (1978). Complementarity versus similarity of traits operating in the choice of marriage and dating partners. Journal of Social Psychology, 105, 147–148.
Todd, P. M., Penke, L., Fasolo, B., & Lenton, A. P. (2007). Different
cognitive processes underlie human mate choices and mate preferences. Proceedings of the National Academy of Sciences of theUnited States of America, 104, 15011–15016.
Toma, C. L., Hancock, J. T., & Ellison, N. B. (2008). Separating factfrom fiction: An examination of deceptive self-presentation on
online dating profiles. Personality and Social Psychology Bulletin, 34, 1023–1036.
Tong, S. T., & Walther, J. B. (2011). Just say ’’no thanks’’: Romantic rejection in computer-mediated communication. Journal ofSocial and Personal Relationships, 28, 488–506.
Tracy, J. (2006, January). Online dating stigma—Is the stigma gone?
Online Dating Magazine. Retrieved from http://www.onlinedatingmagazine.com/columns/2006editorials/04-onlinedatingstigma.html
Turkheimer, E. (2000). Three laws of behavior genetics and what theymean. Current Directions in Psychological Science, 9, 160–164.
Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 79, 281–299.

Online Dating
Utz, S. (2000). Social information processing in MUDs: The development of friendships in virtual worlds. Journal of Online Behavior, 1, 1–23.
Utz, S. (2005). Types of deception and underlying motivation: Whatpeople think. Social Science Computer Review, 23, 49–56.
Valkenburg, P. M., & Peter, J. (2007). Who visits online dating sites?
Exploring some characteristics of online dater. CyberPsychology& Behavior, 10, 849–852.
Valkenburg, P. M., & Peter, J. (2009a). The effects of instant messaging on the quality of adolescents’ existing friendships: A longitudinal study. Journal of Communication, 59, 79–97.
Valkenburg, P. M., & Peter, J. (2009b). Social consequences of theinternet for adolescents. Current Directions in Psychological Science, 18, 1–5.
Veroff, J., Kulka, R. A., & Douvan, E. (1981). Mental health in America: Patterns of helpseeking from 1957 to 1976. New York, NY:
Basic Books.
Visualeconomics.com. (2011). A business of love: Online datingby the numbers. Retrieved from http://www.visualeconomics.
com/a-business-of-love-online-dating-by-the-numbers/
Vohs, K. S., Finkenauer, C., & Baumeister, R. F. (2011). The sum offriends’ and lovers’ self-control scores predicts relationship quality. Social Psychological and Personality Science, 2, 138–145.
Walther, J. B. (1992). Interpersonal effects in computer-mediated interaction: A relational perspective. Communication Research, 19, 52–90.
Walther, J. B. (1995). Relational aspects of computer-mediated communication: Experimental observations over time. Organization
Science, 6, 186–203.
Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23, 3–43.
Walther, J. B. (1997). Group and interpersonal effects in international computer mediated collaboration. Human Communication
Research, 23, 342–369.
Walther, J. B. (2010). Computer-mediated communication. In C. R.
Berger, M. E. Roloff, & D. R. Roskos-Ewoldsen (Eds.), The
handbook of communication science (2nd ed., pp. 489–505).
Thousand Oaks, CA: SAGE.
Walther, J. B., Anderson, J. F., & Park, D. W. (1994). Interpersonal effects in computer-mediated interaction. Communication
Research, 21, 460–487.
Walther, J. B., Loh, T., & Granka, L. (2005). Let me count the ways: Theinterchange of verbal and nonverbal cues in computer-mediatedand face-to-face affinity. Journal of Language and Social Psychology, 24, 36–65.
Walther, J. B., & Parks, M. R. (2002). Cues filtered out, cues filtered in: Computer-mediated communication and relationships.
In M. L. Knapp & J. A. Daly (Eds.), Handbook of interpersonalcommunication (Vol. 3, pp. 529–563). Thousand Oaks, CA:
SAGE.
Walther, J. B., & Tidwell, L. C. (1995). Nonverbal cues in computermediated communication, and the effect of chronemics on relational communication. Journal of Organizational Computing andElectronic Commerce, 5, 355–378.

65
Warren, N. C. (2002). Date . . . or soul mate? How to know if someone isworth pursuing in two dates or less. Nashville, TN: Nelson Books.
Watson, D., & Clark, L. A. (1984). Negative affectivity: The disposition to experience aversive emotional states. Psychological Bulletin, 96, 465–490.
Watson, D., Klohnen, E. C., Casillas, A., Nus Simms, E., Haig, J., &Berry, D. S. (2004). Match makers and deal breakers: Analyses ofassortative mating in newlywed couples. Journal of Personality,
72, 1029–1068.
Wedekind, C., Seebeck, T., Bettens, F., & Paepke, A. J. (1995). MHCdependent mate preferences in humans. Proceedings: Biological
Sciences, 260, 245–249.
Weisfeld, G. E., Russell, R. J. H., Weisfeld, C. C., & Wells, P. A.
(1992). Correlates of satisfaction in British marriages. Ethology
and sociobiology, 13, 125–145.
Weiss, R. L. (1978). The conceptualization of marriage from a behavioral perspective. In T. J. Paolino & B. S. McCrady (Eds.), Marriage and marital therapy: Psychoanalytic, behavioral and systems
theory perspectives (pp. 165–239). New York, NY: Brunner-Mazel.
Whisman, M. A., Uebelacker, L. A., & Weinstock, L. M. (2004).
Psychopathology and marital satisfaction: The importance ofevaluating both partners. Journal of Consulting and Clinical Psychology, 72, 830–838.
White, S. G., & Hatcher, C. (1984). Couple complementarity andsimilarity: A review of the literature. American Journal of Family
Therapy, 12, 15–25.
Whitty, M. T. (2002). Liar, liar! An examination of how open, supportive and honest people are in chat rooms. Computers in Human
Behavior, 18, 343–352.
Whitty, M. T. (2007). Introduction. In M. T. Whitty, A. J. Baker, &J. A. Inman (Eds.), Online matchmaking. New York, NY: Palgrave Macmillan.
Whitty, M. T. (2008). Revealing the “real” me, searching for the“actual” you: Presentations of self on an internet dating site.
Computers in Human Behavior, 24, 1707–1723.
Whitty, M. T., & Buchanan, T. (2009). Looking for love in so many
places: Characteristics of online daters and speed daters. Interpersona: An International Journal of Personal Relationships, 3, 63–86.
Whitty, M. T., & Carr, A. (2006). Cyberspace romance: The psychology of online relationships. New York, NY: Palgrave Macmillan.
Whitty, M. T., & Carville, S. E. (2008). Would I lie to you? Selfserving lies and other-oriented lies told across different media.
Computers in Human Behavior, 24, 1021–1031.
Whitty, M. T., & Gavin, J. (2001). Age/sex/location: Uncovering thesocial cues in the development of online relationships. CyberPsychology & Behavior, 4, 623–630.
Wildermuth, S. M., & Vogl-Bauer, S. (2007). We met on the net:
Exploring the perceptions of online romantic relationship participants. Southern Communication Journal, 72, 211–227.
Wills, J., & Todorov, A. (2006). First impressions: Making up yourmind after a 100-ms exposure to a face. Psychological Science,
17, 592–598.
Winch, R. F. (1958). Mate selection. A study of complementary needs.
New York, NY: Harper & Brothers.

66
Woll, S. B., & Cozby, C. P. (1987). Video-dating and other alternatives to traditional methods of relationship initiation. In W. H.
Jones & D. Perlman (Eds.), Advances in personal relationships(Vol. 1, pp. 69–108). Greenwich, CT: JAI.
Wood, D., & Brumbaugh, C. C. (2009). Using revealed mate preferences to evaluate market force and differential preference explanations for mate selection. Journal of Personality and Social
Psychology, 96, 1226–1244. doi:10.1037/a0015300
Wright, S. C., Aron, A., McLaughlin-Volpe, T., & Ropp, S. A. (1997).
Journal of Personality and Social Psychology, 73, 73–90.
Wu, P., & Chiou, W. (2009). More options leads to more searching
and worse choices in finding partners for romantic relationships

Finkel et al.
online: An experimental study. CyberPsychology & Behavior, 12,
315–318.
Yang, M., & Chiou, W. (2010). Looking online for the best romantic
partner reduces decision quality: The moderating role of choicemaking strategies. Cyberpsychology, Behavior, and Social Networking, 13, 207–210.
Yurchisin, J., Watchravesringkan, K., & McCabe, D. B. (2005). An exploration of identity re-creation in the context of internet dating. Social
Behavior and Personality: An International Journal, 33, 735–750.
Zhou, W., & Chen, D. (2009). Sociochemosensory and emotional
functions: Behavioral evidence for shared mechanisms. Psychological Science, 20, 1118–1124.